Predictive test for melanoma patient benefit from antibody drug blocking ligand activation of the T-cell programmed cell death 1 (PD-1) checkpoint protein and classifier development methods

ABSTRACT

A method is disclosed of predicting cancer patient response to immune checkpoint inhibitors, e.g., an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) or CTLA4. The method includes obtaining mass spectrometry data from a blood-based sample of the patient, obtaining integrated intensity values in the mass spectrometry data of a multitude of pre-determined mass-spectral features; and operating on the mass spectral data with a programmed computer implementing a classifier. The classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of melanoma patients with a classification algorithm and generates a class label for the sample. A class label “early” or the equivalent predicts the patient is likely to obtain relatively less benefit from the antibody drug and the class label “late” or the equivalent indicates the patient is likely to obtain relatively greater benefit from the antibody drug.

PRIORITY

This application claims priority to four U.S. Provisional applications,i.e., Ser. Nos. 62/191,895 filed Jul. 13, 2015; 62/289,587 filed Feb. 1,2016; 62/340,727 filed May 24, 2016 and 62/319,958 filed Apr. 8, 2016.The content of each of four provisional applications, includingappendices thereof, is incorporated by reference herein in theirentirety.

FIELD

This invention relates to a method for predicting in advance oftreatment whether a cancer patient is likely to benefit fromadministration of immune checkpoint inhibitors, including for exampleanti-PD-1 and/or anti-CTLA4 agents, allowing the immune system to attackthe tumor. This application further relates to methods for developing.i.e., training, a computer-implemented classifier from a development setof samples.

BACKGROUND

Melanoma is a type of cancer primarily affecting the skin that developsfrom pigment-containing cells known as melanocytes. The primary cause ofmelanoma is ultraviolet light (UV) exposure in those with low levels ofskin pigment, which causes damage to DNA in skin cells. The UV light maybe from either the Sun or from tanning devices. About 25% of melanomasdevelop from moles. Individuals with many moles, a history of affectedfamily members, or who have poor immune function are all at greater riskof developing a melanoma. A number of rare genetic defects also increasethe risk of developing melanoma. Diagnosis of melanoma is typically doneby visual inspection of any concerning lesion followed by biopsy.

Treatment of melanoma is typically removal by surgery. In those withslightly larger cancers nearby lymph nodes may be tested for spread.Most people are cured if spread has not occurred. In those in whommelanoma has spread, immunotherapy, biologic therapy, radiation therapy,or chemotherapy may improve survival. With treatment, the five-yearsurvival rate in the United States is 98% among those with localizeddisease, but only 17% among those in whom spread has occurred. Melanomais considered the most dangerous type of skin cancer. Globally, in 2012,it occurred in 232,000 people and resulted in 55,000 deaths.

Tumor mutations, including mutations associated with melanoma, createspecific neoantigens that can be recognized by the immune system.Roughly 50% of melanomas are associated with an endogenous T-cellresponse. Cytotoxic T-cells (CT cells, or cytotoxic T lymphocytes(CTLs)) are leukocytes which destroy virus-infected cells and tumorcells, and are also implicated in transplant rejection. CTLs thatexpress the CD8 glycoprotein at their surfaces are also known as CD8+T-cells or CD8+ CTLs. Tumors develop a variety of mechanisms of immuneevasion, including local immune suppression in the tumormicroenvironment, induction of T-cell tolerance, and immunoediting. As aresult, even when T-cells infiltrate the tumor they cannot kill thecancer cells. An example of this immunosuppression in cancer is mediatedby a protein known as programmed cell death 1 (PD-1) which is expressedon the surface of activated T-cells. If another molecule, calledprogrammed cell death 1 ligand 1 or programmed cell death 1 ligand 2(PD-L1 or PD-L2), binds to PD-1, the T-cell becomes inactive. Productionof PD-L1 and PD-L2 is one way that the body naturally regulates theimmune system. Many cancer cells make PD-L1, hijacking this naturalsystem and thereby allowing cancer cells to inhibit T-cells fromattacking the tumor.

One approach to the treatment of cancer is to interfere with theinhibitory signals produced by cancer cells, such as PD-L1 and PD-L2, toeffectively prevent the tumor cells from putting the brakes on theimmune system. Recently, an anti-PD-1 monoclonal antibody, known asnivolumab, marketed as Opdivo®, was approved by the Food and DrugAdministration for treatment of patients with unresectable or metastaticmelanoma who no longer respond to other drugs. In addition, nivolumabwas approved for the treatment of squamous and non-squamous non-smallcell lung cancer and renal cell carcinoma. Nivolumab has also beenapproved in melanoma in combination with ipilimumab, an anti-cytotoxicT-lymphocyte-associated protein 4 (CTLA4) antibody. Nivolumab acts as animmunomodulator by blocking ligand activation of the PD-1 receptor onactivated T-cells. In contrast to traditional chemotherapies andtargeted anti-cancer therapies, which exert their effects by directcytotoxic or tumor growth inhibition, nivolumab acts by blocking anegative regulator of T-cell activation and response, thus allowing theimmune system to attack the tumor. PD-1 blockers appear to free up theimmune system only around the tumor, rather than more generally, whichcould reduce side effects from these drugs.

The current clinical results of anti-PD-1 treatment in melanoma patientsare encouraging and overall results lead to progression free and overallsurvival results that are superior to alternative therapies. However,the real promise of these therapies is related to durable responses andlong-term clinical benefit seen in a subgroup of around 40% of melanomapatients. Some portion of the other ˜60% of patients might do better onalternative therapies. Being able to select which patients derive littlebenefit from anti-PD-1 treatment from pre-treatment samples would enablebetter clinical understanding and enhance the development of alternativetreatments for these patients. There is also considerable cost relatedto these therapies, e.g. the recently approved combination of ipilimumaband nivolumab in melanoma while showing spectacular results is onlyeffective in about 55% of patients while costing around $295,000 pertreatment course. (Leonard Saltz, Md., at ASCO 2015 plenary session:“The Opdivo+Yervoy combo is priced at approximately 4000× the price ofgold ($158/mg)”). This results in a co-pay of around $60,000 forpatients on a standard Medicare plan. Avoiding this cost by selectingthese treatments only for those patients who are likely to benefit fromthem would result in substantial savings to the health care system andpatients. It is also unclear whether the benefit of the combination ofnivolumab and ipilimumab arises from a synergistic effect, or is justthe sum of different patient populations responding to either nivolumabor ipilimumab. In any case having a test for nivolumab benefit wouldshed light on this question.

Much work has been performed to use the expression of PD-L1 measured byimmunohistochemistry (IHC) as a biomarker for selection of anti-PD-1treatments. Correlations between anti-PD-1 efficacy and outcome havebeen observed in some studies but not in others. Of particular issue isthe current lack of standardization and universally accepted cut-offs interms of IHC staining, which renders comparison of such data difficult.Of more fundamental issue is the observation that PD-L1 expressionappears to be a dynamic marker, i.e. IHC expression changes during tumorevolution and during treatment. If one were to use PD-L1 expression viaIHC in a rigorous manner one would require multiple repeat biopsies witha high corresponding risk and cost for patients. In contrast, a serumbased test would not suffer from these effects.

The assignee Biodesix, Inc. has developed classifiers for predictingpatient benefit or non-benefit of certain anticancer drugs using massspectrometry of blood-based samples. Representative patents include U.S.Pat. Nos. 7,736,905, 8,914,238; 8,718,996; 7,858,389; 7,858,390; andU.S. patent application publications 2013/0344111 and 2011/0208433.

SUMMARY

In one aspect, and as will described in more detail in Examples 9 and10, a practical method of guiding melanoma patient treatment withimmunotherapy drugs is disclosed. The method includes the steps of a)conducting mass spectrometry on a blood-based sample of the patient andobtaining mass spectrometry data; (b) obtaining integrated intensityvalues in the mass spectrometry data of a multitude of mass-spectralfeatures; and (c) operating on the mass spectral data with a programmedcomputer implementing a classifier. In the operating step the classifiercompares the integrated intensity values with feature values of areference set of class-labeled mass spectral data obtained fromblood-based samples obtained from a multitude of other melanoma patientstreated with an antibody drug blocking ligand activation of programmedcell death 1 (PD-1) with a classification algorithm and generates aclass label for the sample. The class label “Good” or the equivalent(e.g., Late in the description of Example 10) predicts the patient islikely to obtain similar benefit from a combination therapy comprisingan antibody drug blocking ligand activation of PD-1 and an antibody drugtargeting CTLA4 and is therefore guided to a monotherapy of an antibodydrug blocking ligand activation of PD-1 (e.g., nivolumab), whereas aclass label of “Not Good” or the equivalent (e.g., Early in thedescription of Example 10) indicates the patient is likely to obtaingreater benefit from the combination therapy as compared to themonotherapy of an antibody drug blocking ligand activation of programmedcell death 1 (PD-1) and is therefore guided to the combination therapy.

In still another embodiment, a method of treating a melanoma patient isdisclosed. The method includes performing the method recited above andif the class label is Good or the equivalent the patient is administereda monotherapy of an antibody drug blocking ligand activation of PD-1(e.g., nivolumab), whereas if the class label of “Not Good” or theequivalent is reported the patient is administered a combination therapyan antibody drug blocking ligand activation of PD-1 and an antibody drugtargeting CTLA4, e.g., the combination of nivolumab and ipilimumab.

In one embodiment the mass spectral features include a multitude offeatures listed in Appendix A, Appendix B or Appendix C, or featuresassociated with biological functions Acute Response and Wound Healing(see Examples 1, 6 and 10). In preferred embodiments the classifier isobtained from filtered mini-classifiers combined using a regularizedcombination method, e.g., using the procedure of FIG. 8 or FIG. 54. Theregularized combination method can take the form of repeatedlyconducting logistic regression with extreme dropout on the filteredmini-classifiers. In one example the mini-classifiers are filtered inaccordance with criteria listed in Table 10. As disclosed in Example 9,the classifier may take the form of an ensemble of tumor classifiers(each having different proportions of patients with large and smalltumors) combined in a hierarchical manner. In the illustrated embodimentof Example 9 if any one of the tumor classifiers returns an Early or theequivalent, the label the Not Good or equivalent class label isreported, whereas if all the tumor classifiers return a Late class labelthe Good or equivalent class label is reported.

In this method the relatively greater benefit from the combinationtherapy label means significantly greater (longer) overall survival ascompared to monotherapy.

In another aspect, the reference set takes the form of a set ofclass-labeled mass spectral data of a development set of samples havingeither the class label Early or the equivalent or Late or theequivalent, wherein the samples having the class label Early arecomprised of samples having relatively shorter overall survival ontreatment with nivolumab as compared to samples having the class labelLate.

In preferred embodiments the mass spectral data is acquired from atleast 100,000 laser shots performed on the sample using MALDI-TOF massspectrometry. This methodology is described in Example 1 and in priorpatent documents cited in Example 1,

In one embodiment, as indicated in Examples 6 and 10, the mass-spectralfeatures are selected according to their association with at least onebiological function, for example sets of features which are associatedwith biological functions Acute Response and Wound Healing.

In another aspect, a practical testing method is disclosed forpredicting melanoma patient response to an antibody drug blocking ligandactivation of PD-1. The method includes steps of a) conducting massspectrometry on a blood-based sample of the melanoma patient andobtaining mass spectrometry data; (b) obtaining integrated intensityvalues in the mass spectral data of a multitude of pre-determinedmass-spectral features; and (c) operating on the mass spectral data witha programmed computer implementing a classifier. In the operating stepthe classifier compares the integrated intensity values obtained in step(b) with feature values of a reference set of class-labeled massspectral data obtained from a multitude of other melanoma patientstreated with the drug with a classification algorithm and generates aclass label for the sample. The class label “early” or the equivalentpredicts the patient is likely to obtain relatively less benefit fromthe antibody drug and the class label “late” or the equivalent indicatesthe patient is likely to obtain relatively greater benefit from theantibody drug. The method of generating the classifier used in this testfrom a development set of sample data is described in detail in thisdisclosure.

In another aspect, a machine is described which is capable of predictingmelanoma patient benefit from an antibody drug blocking ligandactivation of the programmed cell death 1 (PD-1). The machine includes amemory storing a reference set in the form of feature values for amultitude of mass spectral features obtained from mass spectrometry ofblood-based samples from a multitude of melanoma patients treated withthe antibody drug. The memory further stores a set of code defining aset of master classifiers each generated from a plurality of filteredmini-classifiers combined using a regularized combination method. Themachine further includes a central processing unit operating on the setof code and the reference set and mass spectral data obtained from ablood-based sample of a melanoma patient and responsively generates aclass label for the blood-based sample, wherein the class label “early”or the equivalent predicts the patient is likely to obtain relativelyless benefit from the antibody drug and the class label “late” or theequivalent indicates the patient is likely to obtain relatively greaterbenefit from the antibody drug.

In another aspect, a system is disclosed for predicting patient benefitfrom an anti-body drug blocking ligand activation of PD-1 in the form ofa mass spectrometer for conducting mass spectrometry of the blood-basedsample of the patient and the machine as recited in the previousparagraph.

In yet another aspect, a method of generating a classifier forpredicting patient benefit from an antibody drug blocking ligandactivation of programmed cell death 1 (PD-1) is disclosed. The methodincludes the steps of:

1) obtaining mass spectrometry data from a development set ofblood-based samples obtained from melanoma patients treated with theantibody drug, in which a mass spectrum from at least 100,000 lasershots is acquired from each member of the set;

2) performing spectral pre-processing operations on the mass spectraldata from the development sample set, including background estimationand subtraction, alignment, batch correction, and normalization;

3) performing the process of FIG. 8 steps 102-150 including generating amaster classifier based on a regularized combination of a filtered setof mini-classifiers;

4) evaluating performance of the master classifiers generated inaccordance with step 3); and

5) defining a final classifier based on the master classifiers generatedin step 3).

In a preferred embodiment, the final classifier includes a training setincluding feature values for a set of features listed in Appendix A,Appendix B, or Appendix C. In one possible embodiment, the method mayinclude the step of deselecting features from the list of features ofAppendix A which are not contributing to classifier performance andperforming steps 3), 4), and 5) using a reduced list of features. Such areduced list of features may take the form of the list of features inone of the sets of Appendix B or the list of features in Appendix C.

In still another embodiment, a method of treating a melanoma patient isdisclosed. The method includes performing the method recited ofpredicting whether the patient will benefit from the antibody drugblocking ligand activation of PD-1 as recited above, and if the patienthas class label of Late or the equivalent for their blood-based samplethen performing a step of administrating the antibody drug to thepatient.

In still another aspect, an improved general purpose computer configuredas a classifier for classifying a blood-based sample from a human cancerpatient to make a prediction about the patient's survival or relativelikelihood of obtaining benefit from a drug is disclosed. Theimprovement is in the form of a memory storing a reference set in theform of feature values for a multitude of mass spectral featuresobtained from mass spectrometry of blood-based samples from a multitudeof melanoma patients treated with an immune checkpoint inhibitor and anassociated class label for each of the blood-based samples in thereference set. The data of blood-based samples form a set used fordeveloping the classifier. The memory further stores a set ofcomputer-executable code defining a final classifier based on amultitude of master classifiers, each master classifier generated from aset of filtered mini-classifiers executing a classification algorithmand combined using a regularized combination method, such as extremedropout and logistic regression. The multitude of master classifiers areobtained from many different realizations of a separation of thedevelopment set into classifier training and test sets. The improvementfurther includes a central processing unit operating on the set of code,the reference set, and mass spectral data obtained from the blood-basedsample of the cancer patient to be tested and generating a class labelfor the blood-based sample.

In one embodiment, the memory stores feature values of at least 50 ofthe features listed in Appendix A. In another embodiment, the memorystores feature values for a reduced set of features, such as thefeatures of one of the approaches listed in Appendix B or the list offeatures of Appendix C.

In one embodiment, the immune checkpoint inhibitor comprises an antibodyblocking ligand activation of PD-1. In another embodiment, the immunecheckpoint inhibitor comprises an antibody blocking ligand activation ofCTLA4.

In still further aspects, a laboratory test center is described whichincludes a mass spectrometer for conducting mass spectrometry of a bloodbased sample from a cancer patient and a machine configured as aclassifier and storing a reference set of mass spectral data asdescribed herein.

We further describe in Example 9 below a general extension of classifierdevelopment to designing the development sets of an ensemble ofclassifiers to explore different clinical groups, for example differentproportions of patients with large and small tumors. In one embodiment,a method of generating an ensemble of classifiers from a set of patientsamples is disclosed, comprising the steps of:

a. defining a plurality of classifier development sample sets from theset of patient samples, each of which have different clinicalcharacteristics (e.g., proportions of patients with large or smalltumors, or other relevant clinical groupings);

b. conducting mass spectrometry on the set of patient samples andstoring mass spectrometry data;

c. using a programmed computer, conducting a classifier developmentexercise using the mass spectral data for each of the development setsdefined in step a. and storing in a memory associated with the computerthe parameters of the classifiers thus generated, thereby generating anensemble of classifiers;

and

d. defining a rule or set of rules for generating a class label for atest sample subject to classification by the ensemble of classifiersgenerated in step c. A method of testing a sample using the ensemble ofclassifiers generated in accordance with this method is also disclosedin Examples 8 and 9. In this method, step b. can be performed before orafter step a.

While we describe in the examples details of our discoveries in melanomaand anti-PD-1 and anti-CTLA4 antibody drugs, our studies of proteincorrelations with classification labels, set forth in great detailbelow, have allowed us to generalize our discoveries. In particular, wecan expect that Example 1, Example 2 and Example 3 classifiers may berelevant/applicable for a broad variety of drugs affecting immunologicalstatus of the patient, such as various immune checkpoint inhibitors,high dose IL2, vaccines, and/or combinational therapy, e.g., anti-PD-1and anti-CTLA4 combination therapy. Furthermore, since effects that aremeasured in serum reflect the organism status as a whole, and thecomplement system, found to be relevant in our discoveries, affectsinnate and adaptive immunity on the global level, not just in a tumorsite, the classifiers are expected to have similar performance indifferent indications (e.g., lung, renal carcinoma), and are notrestricted to melanoma.

In another aspect, a classifier generation method is described,including the steps of:

a) obtaining physical measurement data from a development set of samplesand supplying the measurement data to a general purpose computer, eachof the samples further associated with clinical data;

b) identifying a plurality of different clinical sub-groups 1 . . . Nwithin the development set based on the clinical data;

c) for each of the different clinical sub-groups, conducting aclassifier generation process from the measurement data for each of themembers of the development set that is associated with such clinicalsub-groups, thereby generating clinical sub-group classifiers C1 . . .CN; and

d) storing in memory of a computer a classification procedure involvingall of the classifiers C1 . . . CN developed in step c), each of theclassifiers associated with a reference set comprising samples in thedevelopment set used to generate the classifier and associatedmeasurement data.

In another aspect, a multi-stage classifier is disclosed which includesa programmed computer implementing a hierarchical classifierconstruction operating on mass spectral data of a test sample stored inmemory and making use of a reference set of class-labeled mass spectraldata stored in the memory. The classifier includes (a) a first stageclassifier for stratifying the test mass spectral data into either anEarly or Late group (or the equivalent, the moniker not beingimportant); (b) a second stage classifier for further stratifying theEarly group of the first stage classifier into Early and Late groups (orEarlier and Later groups, or the equivalent), the second stageimplemented if the first stage classifier classifies the test massspectral data into the Early group and the Early class label produced bythe second stage classifier is associated with an exceptionally poorprognosis; and (c) a third stage classifier for further stratifying theLate group of the first stage classifier into Early and Late groups (orEarlier and Later groups, or the equivalent). The third stage classifieris implemented if the first stage classifier classifies the test massspectral data into the Late group, wherein a Late class label (or Lateror the equivalent) produced by the third stage classifier is associatedwith an exceptionally good prognosis.

In one embodiment the third stage classifier comprises one or moreclassifiers developed from one or more different clinical sub-groups ofa classifier development set used to generate the first levelclassifier. In one example, the third stage classifier includes at leastfour different classifiers C1, C2, C3, and C4, each developed fromdifferent clinical sub-groups. In one specific embodiment, wherein themulti-stage classifier is configured to predict an ovarian cancerpatient as being likely or not likely to benefit from platinumchemotherapy, and wherein the classifiers C1, C2, C3 and C4 aredeveloped from the following clinical subgroups:

C1: developed from a subset of patients with non-serous histology orserous histology together with unknown FIGO score;

C2: developed from a subset of patients with serous histology not usedto develop Classifier C1;

C3: developed from a subset of patients with residual tumor aftersurgery;

C4: developed from a subset of patients with no residual tumor aftersurgery.

In yet another aspect, we have discovered a method of generating aclassifier for classifying a test sample from a development set ofsamples, each of the samples being associated with clinical data. Themethod includes the steps of:

(a) dividing the development set of samples into different clinicalsubgroups 1 . . . N based on the clinical data, where N is an integer ofat least 2;

(b) performing a classifier development process (such as for example theprocess of FIG. 8) for each of the different clinical subgroups 1 . . .N, thereby generating different classifiers C1 . . . CN; and

(c) defining a final classification process whereby a patient sample isclassified by the classifiers C1 . . . CN.

In still another aspect, we have discovered a method of generating aclassifier for classifying a test sample, comprising the steps of:

(a) generating a first classifier from measurement data of a developmentset of samples using a classifier development process;

(b) performing a classification of the measurement data of thedevelopment set of samples using the first classifier, thereby assigningeach member of the development set of samples with a class label in abinary classification scheme (Early/Late, or the equivalent); and

(c) generating a second classifier using the classifier developmentprocess with an input classifier development set being the members ofthe development set assigned one of the two class labels in the binaryclassification scheme by the first classifier (e.g., the Early group),the second classifier thereby stratifying the members of the set withthe first class label into two further sub-groups. The method optionallyincludes the steps (d) dividing the development set of samples intodifferent clinical subgroups 1 . . . N where N is an integer of at least2; and (e) repeating the classifier development process for each of thedifferent clinical subgroups 1 . . . N, thereby generating differentthird classifiers C1 . . . CN; and (f) defining a hierarchicalclassification process whereby:

i. a patient sample is classified first by the first classifiergenerated in step a);

ii. if the class label assigned by the first classifier is the classlabel used to generate the second classifier, then classifying thepatient sample with the second classifier; and

iii. if the class label assigned by the first classifier is not theclass label used to generate the second classifier, then classifying thepatient sample with the third classifiers C1 . . . CN; and

iv. assigning a final label as a result of classification steps ii orstep iii.

Example 6 below describes our ability to correlate specific massspectral features with protein functional groups circulating in serumand use such correlations to train a classifier, or to monitor changesin a biological process. In one embodiment, a method of training aclassifier is disclosed, comprising the steps of:

a) obtaining a development set of samples from a population of subjectsand optionally a second independent set of samples from a similar, butnot necessarily identical population of subjects;

b) conducting mass spectrometry on the development set of samples, andoptionally on the second set of samples, and identifying mass spectralfeatures present in the mass spectra of the set(s) of samples;

c) obtaining protein expression data from a large panel of proteinsspanning biological functions of interest for each of the samples in thedevelopment set of samples or optionally each of the samples in thesecond set of samples;

d) identifying statistically significant associations of one or more ofthe mass spectral features with sets of proteins grouped by theirbiological function using Gene Set Enrichment Analysis methods; and

e) with the aid of a computer, training a classifier on the developmentset of samples using the one or more mass spectral features identifiedin step d), the classifier in the form of a set of parameters whichassigns a class label to a sample of the same type as the developmentset of samples in accordance with programmed instructions.

In one embodiment the classifier is in the form of a combination offiltered mini-classifiers which have been subject to a regularizationprocedure. The samples in the development set, and optional secondsample set, are blood-based samples, e.g., serum or plasma samples fromhuman patients.

In another aspect, a classifier development system is disclosed whichincludes a mass spectrometer for conducting mass spectrometry on adevelopment set of samples, and optionally a second independent set ofsamples, to generate mass spectral data, said data including a multitudeof mass spectral features; a platform for conducting a gene setenrichment analysis on the development set of samples, or optionally thesecond independent set of samples, and identifying statisticallysignificant associations of one or more of the mass spectral featureswith sets of proteins grouped by their biological function; and acomputer programmed to train a classifier on the development set ofsamples using the one or more mass spectral features identified by theplatform, the classifier in the form of a set of parameters whichassigns a class label to a sample of the same type as the developmentset of samples in accordance with programmed instructions. In preferredembodiments the development set of samples, and optional secondindependent set of samples, are blood-based samples from humans. Forexample, the blood-based samples for the development sample set areobtained from melanoma patients obtained in advance of treatment with animmunotherapy drug, e.g., nivolumab.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a Kaplan-Meier plot for time-to-progression (TTP)and FIG. 1B illustrates a Kaplan-Meier plot for overall survival (OS)for the cohort of 119 melanoma patients treated with nivolumab withavailable clinical data and spectra from pre-treatment samples.

FIGS. 2A and 2B are Kaplan-Meier plots of time-to-event data (TTP andOS), respectively, for all 119 patients with available clinical data andspectra from pretreatment samples by prior treatment (no prioripilimumab, prior ipilimumab). Differences in outcome were notstatistically significant.

FIGS. 3A and 3B are Kaplan-Meier plots of time-to-event data (TTP andOS), respectively, for all 119 patients with available clinical data andspectra from pretreatment samples showing relatively good outcomes forpatients in cohort 5.

FIG. 4A and FIG. 4B are Kaplan-Meier plots showing time-to-event datafor all 119 patients with available clinical data and spectra frompretreatment samples split into development (N=60) and validation (N=59)sets (“DEV1”); FIGS. 4C and 4D are Kaplan-Meier plots of time-to-eventdata for all 119 patients with available clinical data and spectra frompretreatment samples split into development (N=60) and validation (N=59)sets for a second split of samples into development and validation sets(“DEV2”).

FIG. 5 is a plot of bin normalization scalars as a function of diseasecontrol (DC); the bin method is used to compare normalization scalarsbetween clinical groups of interest to ensure that windows useful forclassification are not used for partial ion current normalization.

FIG. 6 is a plot of mass spectra from a multitude of samples showingseveral feature definitions defined within an m/z range of interest;different clinical performance groups are shown in contrasting lineconventions.

FIG. 7 is a plot of bin normalization scalars as a function of DC for apartial ion current normalization performed on the features in the finalfeature table used for classifier generation.

FIGS. 8A-8B are a flow chart of a classifier development process we usedto develop the melanoma/nivolumab classifiers of this disclosure fromthe sample set of 119 serum samples from melanoma patients in the trialof nivolumab.

FIGS. 9-12 are Kaplan-Meier plots showing classifier performance for theclassifiers we developed in Example 1 using the classifier developmentprocedure of FIG. 8.

FIGS. 9A and 9B show Kaplan-Meier plots for OS and TTP, respectively, byEarly and Late classification groups for “approach 1” (see table 10) forthe development set and FIGS. 9C and 9D show the Kaplan-Meier plots forOS and TTP, respectively, by Early and Late classification groups forapproach 1 (see table 10) for the validation set.

FIGS. 10A and 10B show Kaplan-Meier plots for OS and TTP, respectively,by Early and Late classification groups for “approach 2” (see table 10)for the development set, FIGS. 10C and 10D show the Kaplan-Meier plotsfor OS and TTP, respectively, by Early and Late classification groupsfor approach 2 (see table 10) for the validation set.

FIGS. 11A and 11B show Kaplan-Meier plots for OS and TTP, respectively,by Early and Late classification groups for “approach 3” (see table 10)for the development set and

FIGS. 11C and 11D show Kaplan-Meier plots for OS and TTP, respectively,by Early and Late classification groups for approach 3 (see table 10)for the validation set.

FIGS. 12A and 12B show Kaplan-Meier plots for OS and TTP by Early andLate classification groups for “approach 4” (see table 10) for thedevelopment set, and FIGS. 12C and 12D show the Kaplan-Meier plots forOS and TTP by Early and Late classification groups for approach 4 (seetable 10) for the validation set.

FIGS. 13A and 13B illustrate Kaplan-Meier plots for OS and TTP,respectively, by Early and Late classification groups for a firstapproach shown in Table 13 applied to the whole set of 119 samples asdevelopment set (100) for classifier generation in accordance with FIG.8. FIGS. 13C and 13D illustrate Kaplan-Meier plots for OS and TTP,respectively, by Early and Late classification groups for a secondapproach shown in Table 13 applied to the whole set of 119 samples asdevelopment set (100) for classifier generation in accordance with FIG.8.

FIG. 14 is a Kaplan-Meier plot for the analysis of the Yale cohort ofpatients treated with anti-PD-1 antibodies, an independent sample setused for validation of the classifiers of Example 1 developed using FIG.8.

FIG. 15 is an illustration of a laboratory testing center including amass spectrometer and a machine in the form of a general purposecomputer for predicting melanoma patient benefit from antibody drugsblocking ligand activation of PD-1.

FIGS. 16A and 16B are Kaplan-Meier plots of progression free survival(PFS) and overall survival (OS), respectively, for all 173 non-smallcell lung cancer (NSCLC) patients of the ACORN NSCLC cohort withavailable clinical data and spectra from pretreatment samples. The ACORNNSCLC cohort was used to develop and tune the classifier of Example 2 tobe predictive of melanoma patient benefit of nivolumab.

FIGS. 17A and 17B are plots of progression free survival PFS and overallsurvival OS for the three different subsets of the ACORN NSCLC cohort.One subset (“additional filtering” in the figures) was used to filtermini-classifiers in generation of the classifier of Example 2. Onesubset (“test” in the figures) was used for testing classifierperformance together with the melanoma development set samples. Theother subset (“validation” in the figures) was used as an internalvalidation set, in addition to a melanoma subset already held for thatpurpose. The similarity in the plots of FIGS. 17A and 17B indicate thatthe survival data for the “additional filtering” subset wasrepresentative of the other subsets.

FIGS. 18A-18H are Kaplan-Meier plots of OS and TTP or PFS for thedevelopment and internal validation sets of samples. In particular,FIGS. 18A-18D are the plots of OS and TTP for the development andvalidation sets of the melanoma/nivolumab cohort. Note that FIGS.18A-18D show the separation in the survival plots of the samples labeledEarly and Late by the classifier of Example 2. FIGS. 18E-18H are theplots of OS and PFS for the development and validation sets of the ACORNNSCLC chemotherapy cohort. Note that the plots in the ACORN NSCLC cohort(FIGS. 18E-18H) show similar OS and PFS for samples labeled Early andLate by the classifier of Example 2.

FIG. 19 is a Kaplan-Meier plot for an independent validation cohort ofpatients treated with anti-PD-1 antibodies (the Yale anti-PD-1 cohort),showing the separation of OS plots for patient samples labeled Early andLate by the classifier of Example 2.

FIG. 20 is a Kaplan-Meier plot for an independent Yale validation cohortof melanoma patients treated with anti-CTLA4 antibodies, showing theseparation of OS plots for patient samples labeled Early and Late by theclassifier of Example 2.

FIGS. 21A and 21B are Kaplan-Meier plots of OS (FIG. 21A) and diseasefree survival (DFS, FIG. 21B) for the independent validation cohort ofpatients with ovarian cancer treated with platinum-doublet chemotherapyafter surgery. Like the plots of FIGS. 18E-18G, the plots of FIGS. 21Aand 21B show a lack of separation in OS and DFS for the ovarian cancerpatients whose samples are classified as Early or Late by the classifierof Example 2.

FIG. 22 is a Kaplan-Meier plot of overall survival for the Yaleanti-CTLA4 cohort.

FIG. 23 is a Kaplan-Meier plot for the Yale cohort of melanoma patientstreated with anti-CTLA4 antibodies for the “full-set” classifier ofExample 1, see Table 13 below. FIG. 23 is similar to FIG. 20 in thatboth figures show that the classifiers of Example 1 and Example 2 ofthis disclosure are able to predict melanoma patients having relativelybetter or worse outcomes on anti-CTLA4 antibody treatment. FIGS. 23A and23B are Kaplan-Meier plots of classifier performance of a secondanti-CTLA4 antibody classifier, which was developed from the Yaleanti-CTLA4 cohort.

FIGS. 24A and 24B are Kaplan-Meier plots of OS and PFS, respectively,for the ACORN NSCLC cohort used in the development of the classifier ofExample 2.

FIGS. 25A and 25B are Kaplan-Meier plots of OS and PFS, respectively,for the ACORN NSCLC cohort, by classification produced by the “full-set”classifier of Example 1. Note the clear separation in the overallsurvival plot of FIG. 25A between the samples classified as Early andLate by the full-set classifier of Example 1. A separation in PFSbetween the Early and Late classified samples is also shown in FIG. 25B.

FIGS. 26A and 26B are Kaplan-Meier plots for the ACORN NSCLC cohort byclassification and VeriStrat label (assigned in accordance with theclassifier and training set of U.S. Pat. No. 7,736,905). Note that thereis essentially no separation between the VeriStrat Good (VS-G) and Poor(VS-P) samples classified Early by the full-set classifier of Example 1,and the clear separation in the survival plots between those classifiedLate and those classified Early by the full-set classifier of Example 1.

FIGS. 27A and 27B are Kaplan-Meier plots of OS and DFS for the ovariancancer chemotherapy cohort of 138 patients used for internal validationof the classifier of Example 2.

FIGS. 28A and 28B are Kaplan-Meier plots for OS and DFS, respectively,of the ovarian cancer chemotherapy cohort by classification produced bythe “full-set” classifier of Example 1. Note the clear separation in theoverall survival plot of FIG. 28A between the samples classified asEarly and Late by the full-set classifier of Example 1. A clearseparation in DFS between the Early and Late classified samples is alsoshown in FIG. 28B. Thus, the Figures demonstrate the ability of thefull-set classifier of Example 1 to predict ovarian cancer survival onchemotherapy.

FIGS. 29A and 29B are Kaplan-Meier plots of the ovarian cancerchemotherapy cohort by classification and VeriStrat label. Note thatthere is essentially no separation between the samples classified Earlyand tested as VeriStrat Good (VS-G) and Poor (VS-P) by the full-setclassifier of Example 1, and the clear separation in the survival plotsbetween those classified Late and those classified Early by the full-setclassifier of Example 1. As in the NSCLC cohort, it is apparent thatoutcomes are similar between VeriStrat subgroups within the group ofpatients classified as Early.

FIGS. 30A-30F are Kaplan-Meier plots of classifications by early andlate groups produced by the full set classifier of Example 1 initially(advance of treatment, “baseline” herein) shown in FIGS. 30A and 30B,after 7 weeks of treatment (WK7) shown in FIGS. 30C and 30D, and after13 weeks (WK13), shown in FIGS. 30E and 30F.

FIGS. 31A and 31B are Kaplan-Meier plots of overall survival and time toprogression (TTP), respectively, grouped by the triplet of baseline,WK7, and WK13 classifications produced by the Example 1 full setclassifier. There were too few patients with other label combinationsfor a meaningful analysis.

FIGS. 32A and 32B are Kaplan-Meier plots of overall survival and time toprogression (TTP), respectively, which show the outcomes when thepatients are grouped according to their triplet of baseline, WK7, andWK13 classifications produced by the Example 2 classifier. (E=Early,L=Late)

FIGS. 33A and 33B are Kaplan-Meier plots of a subset of 104 patients ofthe development sample set of Example 1, showing the Early and Latelabeled patients from the original classifications produced by theExample 1 full set classifier (“Original”) and the classificationsproduced by new “large” and “small” tumor classifiers (“TSAdjusted”).

FIGS. 34A and 34B are Kaplan-Meier plots of overall survival and time toprogression (TTP), respectively, of 47 Early patients (classifiedaccording to the Example 1 full-set Approach 1 classifier) classifiedinto Earlier and Later groups by a classifier using these 47 samples asits development set. The patients in the Earlier classification groupare removed from the development sample set in generating classifierswhich take into account tumor size.

FIGS. 35A and 35B are Kaplan-Meier plots of all 119 samples in theoriginal development set of Example 1, with the large tumors classifiedby a classifier developed using only large tumors, the small tumorsclassified by a classifier developed using only small tumors and earlyprogressing patients (the Early/Earlier patients of FIG. 34) classifiedas Early (their original Example 1 full-set Approach 1 classifications).These groups are labeled as “Final” and compared with the groupsproduced by the Example 1 full-set classifier (“Original”).

FIGS. 36A and 36B are waterfall plots showing the class identificationsfor melanoma patients who had either increasing or decreasing tumor sizeover the course of treatment with nivolumab. FIG. 36A shows the data forthe large and small tumor classifiers classifying the members of thedevelopment sample set (after removal of the fast progressing patients)and fast progressing patients with available tumor size change dataclassified as Early (“Final”), whereas FIG. 36B shows the data for thefull-set classifier of Example 1.

FIG. 37 is a flow-chart showing the process of development of small andlarge tumor classifiers from a development sample set.

FIG. 38 is a flow-chart showing how either the large and small tumorclassifier generated in accordance with FIG. 37 is used to test a sampleof a cancer patient, depending on whether the patient has a large orsmall tumor.

FIG. 39 is a flow-chart showing one method for removing from thedevelopment set the fast progressing samples, step 3702 of FIG. 37.

FIGS. 40A and 40B are Kaplan-Meier plots of overall survival and time toprogression, respectively, for classifications generated by an ensembleof seven classifiers, each of which are based on different classifierdevelopment sets having different clinical groupings (based on tumorsize in this example) and generated in accordance with FIG. 8. Theensemble of classifiers is generated from the 119 patient samplesdescribed in Example 1. A set of rules define a class label from labelsproduced by the ensemble of classifiers, such as “Bad”, “Good” and“Other,” which can be used to guide melanoma patient treatment asexplained in Example 9 below.

FIG. 41 is a Kaplan-Meier plot of overall survival for theclassifications obtained by a test composed of the ensemble of sevenclassifiers of Example 9 for 30 samples in an anti-PD-1 treatedvalidation cohort.

FIG. 42 is a Kaplan-Meier plot of overall survival by Good and NotGoodclass labels produced by an ensemble of seven classifiers of Example 8for melanoma patients treated with anti-PD1 monotherapy (nivolumab) aswell as melanoma patients treated with both ipilimumab and nivolumabcombination therapy. The plot shows that nivolumab patients having theclass label Good have very similar survival as compared to patients withthe nivolumab+ipilimumab combination therapy.

FIG. 43 is an example of a plot of running sum (RS) score (RS(S_(l,p)))calculated in a gene set enrichment analysis (GSEA) for one protein setS_(l).

FIGS. 44A and 44B are examples of the null distributions for the twodefinitions of the enrichment scores (ES) used in a GSEA, showing thecalculated ES and the regions assessed to determine the p value.

FIGS. 45A and 45B are “heat maps”, namely plots of p values generated byGSEA associating all 351 defined mass spectral features (Appendix A) inour nivolumab study of Examples 1 and 6 with protein functional groups.FIG. 45A is the heat map for ES definition 1, and FIG. 45B is the heatmap for ES definition 2. Only every 5^(th) spectral feature is labeledon the x axis.

FIG. 46 is a schema of a final classification procedure of Example 6.

FIGS. 47A and 47B illustrate Kaplan-Meier plots of overall survival (OS)(FIG. 47A) and time to progression (TTP) (FIG. 47B) for themelanoma/immunotherapy new classifier development (NCD) cohort byclassification group from Classifier 1 of Example 6.

FIGS. 48A and 48B illustrate Kaplan-Meier plots of OS and TTP,respectively, for the 68 samples not classified as “Early” by Classifier1, by classification group from Classifier 2 of Example 6.

FIGS. 49A and 49B illustrate Kaplan-Meier plots of OS and TTP,respectively, for all 119 samples by overall classification produced bythe classifier schema of FIG. 46.

FIGS. 50A-50D illustrates Kaplan-Meier plots comparing the performanceof the classifier developed in Example 6 (“Current”) with thosedeveloped in Example 1 (FIGS. 50A and 50B, OS and TTP,respectively)(“IS2”) and Example 8 (FIGS. 50C and 50D, OS and TTP,respectively)(“IS6”).

FIG. 51 is a Kaplan-Meier plot of OS for the validation cohort ofExample 6 by classification group.

FIG. 52 is a block diagram of a classifier training system including amass spectrometer for conducting mass spectrometry on a development setof samples (not shown, e.g., serum or blood samples), a GSEA platform,including protein assay system and computer with GSEA analysis module,and a computer programmed to conduct a classifier training procedureusing sets of mass spectral peaks associated with a particular proteinfunction group of a biological process of interest in the developmentset of samples.

FIGS. 53A and 53B are Kaplan-Meier plots of time to event data fordisease free survival (DFS, FIG. 53A) and overall survival (OS, FIG.53B) for a cohort of 138 ovarian cancer patients with available clinicaldata and mass spectral data, which were used to develop the ovarianclassifiers of Example 9.

FIGS. 54A and 54B are a flow chart showing a computer-implementedprocedure for developing a classifier from a development sample set. InExample 9, the procedure of FIGS. 54A and 54B (up to and including step350) was performed several different times for different configurationsor subsets of the development sample set to result in the creation of atiered or hierarchical series of classifiers (referred to as ClassifiersA, B and C), as will be explained in more detail in the description ofExample 9.

FIGS. 55A and 55B are Kaplan-Meier plots of time to event data for the129 patients in the ovarian development sample set of Example 9 withavailable clinical data, DFS>1 month, and mass spectral data frompretreatment samples, showing the plots for a split of the sample setinto development (N=65) and validation (N=64) sets. FIG. 55A shows theplot of DFS; FIG. 55B shows the plot for OS. Note that the plots for thedevelopment and validation sample sets are essentially the same.

FIGS. 56A-56D are Kaplan-Meier plots of OS and DFS by Early and Lateclassification groups produced by the first tier or “Classifier A”classifier of Example 9, for the 129 patients split into development andvalidation sets. FIG. 56A is a plot of OS for the development set; FIG.56B is a plot of DFS for the development set; FIG. 56C is a plot of OSfor the validation set; FIG. 56D is a plot of DFS for the validationset.

FIGS. 57A and 57B are Kaplan-Meier plots of OS and DFS by Early and Lateclassification groups, for the “Classifier A” of Example 9 run on all138 samples.

FIGS. 58A and 58B are Kaplan-Meier plots of OS and DFS, respectively, byclassification group produced by the Classifier B classifier of Example9, for the subset of the development set of samples which were used todevelop the Classifier B.

FIG. 59 is a flow chart showing a process for generating a second tierclassifier (“Classifier B”) from those development set samples that wereclassified as “Early” by the first tier “Classifier A” classifier ofExample 9.

FIG. 60 is a flow chart showing a process for generating a third tierclassifier C of Example 9; in this particular example the third tierconsists of a several different classifiers each based on a differentand clinically distinct subset of the development sample set.

FIG. 61 is a diagram showing the construction of a third tier ClassifierC, and how it could be used to generate a “Good’ class label based onthe results of classification by each of the members of the third tier.

FIG. 62 is a diagram or schema showing the construction of a finalclassifier composed of a three-stage hierarchical classifier.

FIG. 63 is a diagram or schema showing the construction of analternative final classifier in which the third stage of the three-stagehierarchical classifiers is made up of four individual classifiersdeveloped from clinically distinct subgroups.

FIGS. 64A and 64B are Kaplan-Meier plots of OS and DFS, respectively byclassification group produced on the development sample set using thefinal classifier construction of FIG. 63 and Example 9.

FIGS. 65A-65C are plots of the average of the percentage of the“PROSE-erlotinib” data set variances explained by each principalcomponent (PC) as a function of the PC index (descending order ofvariance) from Example 10, for biological functions Acute Response (FIG.65A), Wound Healing (FIG. 65B) and Complement system (FIG. 65C).

FIGS. 66A-66D are distributions of the Acute Response Score in foursample sets described in Example 10. In the results shown in FIG.66A-66D, twenty nine mass spectrometry features were determined to becorrelated with the AR biological function and were used in calculationof the corresponding Score.

FIGS. 67A-67D are distributions of the Acute Response Score in foursample sets: “PROSE-erlotinib” (85 samples with available IS2 labels),FIG. 67A; “PROSE-chemo” (122 samples with available IS2 labels), FIG.67B; “Moffitt”, FIG. 67C; and “Moffitt-Week7”, FIG. 67D. FIGS. 67A-67Dalso shows the Scores split by IS2 classification label for the samplesfrom the Example 1 classifier.

FIGS. 68A-68B are Kaplan-Meier plots for OS, PFS for a “PROSE-erlotinib”sample set, FIGS. 68C-68D are Kaplan-Meier plots for OS and PFS in a“PROSE-chemo” set, FIGS. 68E-68F are Kaplan-Meier plots for OS and TTPin a “Moffitt” set, by group defined according to the AR Score thresholddefined as illustrated in the Figures. The corresponding numbers ofsamples in each group, hazard ratios (HRs), log-rank p-values andmedians are shown below each plot.

FIGS. 69A-69D are illustrations of the evolution of the Acute ResponseScore over time for 107 patients in both the Moffitt and Moffitt-Week7sets, grouped by combination of IS2 label at baseline (before treatment)and week 7 (after treatment). Each line in the plots represent the scoreof an individual patient. FIG. 69A is the plot for patients with classlabel Early at both baseline and week 7; FIG. 69B is the plot for thepatients with class labels Late at both baseline and week 7; FIG. 69C isthe plot for patients with class label Early at baseline and Late atweek 7, and FIG. 69D is a plot for patients with class label Late atbaseline and Early at week 7.

FIG. 70A-70C are plots of evolution of the Acute Response Score for the107 patients with samples both in the “Moffitt” and the “Moffitt-Week7”sets, grouped by treatment response. Each line represents one singlepatient. FIG. 70A is the plot for the PD: progressive disease treatmentresponse; FIG. 70B is the plot for the PR: partial response totreatment, and FIG. 70C is the plot for SD: stable disease treatmentresponse.

FIGS. 71A and 71B are Kaplan-Meier plots of OS and TTP, respectively forthe samples both in the “Moffitt” and the “Moffitt-Week7” sets groupedby whether the AR score increased or had a small change or decrease. Theplots show the change in AR score has prognostic significance.

FIG. 72A-72D are the plots of distribution of the Wound Healing Scoreacross four sample sets described in Example 10.

FIG. 73A-73D are illustrations of the evolution of the Wound HealingScore over time for 107 patients in both the Moffitt and Moffitt-Week7sets, grouped by combination of IS2 label at baseline (before treatment)and week 7 (after treatment). Each line in the plots represents thescore of an individual patient. FIG. 73A is the plot for patients withclass label Early at both baseline and week 7; FIG. 73B is the plot forthe patients with class labels Late at both baseline and week 7; FIG.73C is the plot for patients with class label Early at baseline and Lateat week 7, and FIG. 73D is a plot for patients with class label Late atbaseline and Early at week 7.

FIG. 74A-74C are plots of evolution of the Wound Healing Score for the107 patients with samples both in the “Moffitt” and the “Moffitt-Week7”sets, grouped by treatment response. Each line represents one singlepatient. FIG. 74A is the plot for the PD: progressive disease treatmentresponse; FIG. 74B is the plot for the PR: partial response totreatment, and FIG. 74C is the plot for SD: stable disease treatmentresponse.

FIGS. 75A-75D are the plots of distribution of the Complement SystemScore across four sample sets described in Example 10.

FIG. 76A-76D are illustrations of the evolution of the Complement SystemScore over time for 107 patients in both the Moffitt and Moffitt-Week7sets, grouped by combination of IS2 label at baseline (before treatment)and week 7 (after treatment). Each line in the plots represents thescore of an individual patient. FIG. 76A is the plot for patients withclass label Early at both baseline and week 7; FIG. 76B is the plot forthe patients with class labels Late at both baseline and week 7; FIG.76C is the plot for patients with class label Early at baseline and Lateat week 7, and FIG. 76D is a plot for patients with class label Late atbaseline and Early at week 7.

FIG. 77A-77C are plots of evolution of the Wound Healing Score for the107 patients with samples both in the “Moffitt” and the “Moffitt-Week7”sets, grouped by treatment response. Each line represents one singlepatient. FIG. 77A is the plot for the PD: progressive disease treatmentresponse; FIG. 77B is the plot for the PR: partial response totreatment, and FIG. 77C is the plot for SD: stable disease treatmentresponse.

FIGS. 78A-78D are Kaplan-Meier plots for OS, PFS (“PROSE-chemo”), FIGS.78A-78B, and OS and TTP (“Moffitt” set), FIGS. 78C and 78D, by groupdefined according to the AR score threshold defined by tertiles in thePROSE set. The corresponding number of samples in each group, hazardratios (HRs), log-rank p-values and medians are shown below each plot.

FIGS. 79A and 79B are Kaplan-Meier plots for classification groups Earlyand Late, obtained from a classifier developed in accordance with theprocedure of FIG. 8 but instead of using mass spectral features, thefeatures used for classification are the Biological Function Scores ofExample 10, in this example the Acute Response Score, the Wound HealingScore and the Complement Score.

FIG. 80 is a schematic illustration of system for generating thebiological function scores of Example 10 for a sample set.

FIG. 81 is a flow chart showing the steps for calculating the biologicalfunction scores of Example 10 from mass spectrometry data.

FIG. 82 is an illustration of a partial feature table for sample set ss,F^(ss) which is used in the calculation of a biological function scoreof Example 10.

FIG. 83 is an illustration of an average first principal componentvector û₁ which is used in the calculation of a biological functionscore of Example 10.

DETAILED DESCRIPTION

A practical test (method) is disclosed in this document for predictingwhether a cancer patient is likely to be benefit from administration ofimmune checkpoint inhibitors such as a monoclonal antibody drug blockingligand activation of PD-1 on activated T cells, e.g., nivolumab. Themethod makes use of the mass spectrum of the patient's serum or plasmasample acquired pre-treatment, and a general purpose computer configuredas a classifier which assigns a class label to the mass spectrum. Theclass label can take the form of “early” or the equivalent, or “late” orthe equivalent, with the class label “late” indicating that the patientis a member of a class of patients that are likely to obtain relativelygreater benefit from the drug than patients that are a member of theclass of patients having the class label “early.” The particular monikerused for the class label is not particularly important.

Overall survival is a primary indicator for assessing the benefit ofantibody drugs blocking ligand activation of PD-1. Hence, whenconsidering the meaning of the labels Early and Late, in one preferredembodiment the “relatively greater benefit” associated with the Latelabel means a patient whose sample is assigned the Late label is likelyto have significantly greater (longer) overall survival than a patientwith the Early class label.

The term “antibody drug blocking ligand activation of PD-1” is meant toinclude not only antibodies that bind that PD-1, but also those thatbind to the ligands (PD-L1 and PD-L2). Anti-PD-L1 or anti-PD-L2monoclonal antibodies (mAbs) would also block ligand activation of PD-1.The term “immune checkpoint inhibitors” is meant to include those thatblock PD-1 as well as those that block CLTA-4. The term immunecheckpoints inhibitors or “checkpoint blockers” is defined asimmunomodulatory mAbs that target CTLA4-like receptors and theirligands. See Galluzzi L, Kroemer G, Eggermont A. Novel immune checkpointblocker approved for the treatment of advanced melanoma. Oncoimmunology2014; 3: e967147. FDA-approved agents of this type include ipilimumab(anti-CTLA4), nivolumab (anti-PD-1), and pembrolizumab (anti-PD-1).Several publications/abstracts released during the last 13 monthsreported the results of clinical trials involving additional checkpointblockers, such as the CTLA4-targeting mAb tremelimumab, thePD-1-targeting mAb pidilizumab, and the PD-L1-targeting mAbs MEDI4736(durvalumab), MPDL3280A (atezolizumab), and MSB0010718C (avelumab). SeeBuqué A, Bloy N, Aranda F, Castoldi F, Eggermont A, Cremer I, Fridman WH, Fucikova J, Galon J, Marabelle A, Spisek R, Tartour E, Zitvogel L,Kroemer G, Galluzzi L. Trial Watch: Immunomodulatory monoclonalantibodies for oncological indications Oncoimmunology. 2015 Mar. 2;4(4). The content of the above-cited scientific publications isincorporated by reference herein.

Example 1 explains the development of a classifier from amelanoma/nivolumab sample set and provides details of classifierperformance.

Example 2 explains a redevelopment of the classifier of Example 1 thathas been tuned to be more predictive and less prognostic for patientbenefit from nivolumab.

Example 3 explains the development of a classifier that is predictivefor melanoma patient benefit from anti-CTLA4 antibodies, another immunecheckpoint inhibitor.

Example 4 explains how the classifier developed in accordance withExample 1 is also able to predict whether non-small cell lung cancer(NSCLC) and ovarian cancer patients are likely to have relatively higheror lower overall survival from chemotherapy.

Example 5, and FIG. 15, describes a practical testing environment forconducting the tests of this disclosure on a blood-based sample from apatient in advance of treatment.

Example 6 describes our studies of proteins which are correlated to theEarly and Late class labels in the Example 1 and Example 2 classifiersusing Gene Set Enrichment Analysis, which allow us to generalize ourdiscoveries to other immune checkpoint inhibitors and other types ofcancers, as well as generate classifiers based on mass spectral featuresassociated with particular protein functional groups.

Example 7 describes longitudinal studies from patient samples of Example1 and how changes in class label produced by our classifiers can beused, inter alia, to monitor treatment efficacy and guide treatment.

Example 8 describes an ensemble of classifiers generated from differentclinical subsets of the Example 1 development sample set population, inthis example, melanoma patients with large and small tumors.

Example 9 provides further examples of development of an ensemble ofclassifiers from clinically different development sets, including afirst example of an ensemble of classifiers from the nivolumab/melanomaset of Example and a second example from study of ovarian cancerpatients treated with chemotherapy.

Example 10 describes a methodology for measurement of a biologicalfunction score using mass spectrometry data. Example 10 furtherdescribes the measurement of biological functions scores in fourdifferent sample sets, each blood-based samples from humans with cancer.Example 10 describes how the scores can be used to guide treatment andto build a classifier using biological function scores as features forclassifier training, e.g., using the procedure of FIG. 8. Example 10also builds on the discoveries described in Example 6, includingcorrelation of biological functions with mass spectrometry peaks.

Example 1 Classifier and Method for Predicting Melanoma Patient Benefitfrom Antibody Drug Blocking Ligand Activation of PD-1

We obtained samples to develop a classifier of Example 1 from a clinicaltrial of nivolumab in treatment of melanoma. We describe briefly thistrial below. We then describe the patient samples which we obtained, themass spectrometry methods used to obtain spectra from the samples,including spectra processing steps. These mass spectral procedures arepreferably in accordance with the so-called “Deep MALDI” methoddescribed in U.S. Pat. No. 9,279,798, the content of which isincorporated by reference herein. We then describe in detail aclassifier generation process which was used to define a finalclassifier which can assign class labels to spectra in accordance withthe test. We also describe below the results of the classifiergeneration process and demonstrate its ability to assign class labels tomass spectra from blood-based samples which predict whether the patientproviding the sample is likely to obtain relatively greater or lesserbenefit from the antibody drug.

The classifier generation method uses what we have called “combinationof mini-classifiers with dropout regularization”, or CMC/D, described inpending U.S. patent application Ser. No. 14/486,442 filed Sep. 15, 2014,published as U.S. patent application publication 2015/0102216, thecontent of which is incorporated by reference herein. This procedure fordeveloping a classifier is also referred to herein as DIAGNOSTIC CORTEX,a trademark of Biodesix, Inc. See the discussion of FIG. 8 below.Applying this procedure to pre-treatment serum spectra from melanomapatients obtained using Deep MALDI spectral acquisition we haveidentified clinical groups “Early” and “Late.” These groups are showingsignificant differences in outcome (both time to progression (TTP) andoverall survival (OS)) following treatment with an anti-PD-1 treatment,nivolumab. See FIGS. 9-14 and the discussion below. We have presented atest procedure to identify these groups from pre-treatment samples, andvalidated the results in internal validation sets, and in an externalvalidation set (see FIG. 14).

Patients whose serum classifies as “Early” exhibit significantly fasterprogression and shorter survival than patients whose serum classifies as“Late” making this test suitable as a biomarker for nivolumab therapy.The clinical groups “Early” and “Late” are not associated with PD-L1expression, and our classification remains a significant predictor foroutcome (both TTP and OS) even when other clinical attributes areincluded in a multivariate analysis.

While a correlative approach to test development does not easily lenditself to a deep understanding of the biology underlying the differencebetween the identified groups, we have done some initial work relatingthe two different groups to differences in acute phase reactants and thecomplement system. These studies are set forth in Example 1 and later inthis document in Example 6. The success of this project exemplifies thepower of the combination of Deep MALDI spectral acquisition and ourinventive classifier development method in the construction ofclinically useful, practical tests.

Clinical Trial

The trial from which samples were available for new classifierdevelopment was a study of nivolumab with or without a peptide vaccinein patients with unresectable stage III or stage IV melanoma. The trialis described in the paper of J. S. Weber, et al., Safety, Efficacy,Biomarkers of Nivolumab With Vaccine in Ipilimumab-Refractory or -NaïveMelanoma, J. Clin. Oncol. vol. 31 pp. 4311-4318 (2013), the content ofwhich is incorporated by reference herein. Patients enrolled in thetrial had experienced progression after at least one prior therapy, butno prior PD-1 or PD-L1 treatment. The trial consisted of 6 patientcohorts. Cohorts 1-3 enrolled patients who were ipilimumab-naïve, whilepatients in cohorts 4-6 had progressed after prior ipilimumab therapy.Cohorts 1-5 received the peptide vaccine in addition to nivolumab andcohort 6 received nivolumab alone. Cohort 5 enrolled only patients whohad experienced grade 3 dose-limiting toxicities on ipilimumab therapy,while patients in cohorts 4 and 6 could only have experienced at mostgrade 2 dose-limiting ipilimumab toxicities. Cohorts 1-3 differed in thenivolumab dose (1 mg/kg, 3 mg/kg or 10 mg/kg). The number of priortreatment regimens was not restricted. All patients had ECOG performancestatus (PS) 0-1.

For the purpose of this new classifier development project, the dose ofnivolumab and whether or not peptides were given in addition tonivolumab is not considered to be significant.

Samples

The samples available for this study were pretreatment serum samples.Clinical data and spectra were available from 119 patients. Availableoutcome data included time-to-progression (TTP), overall survival (OS),and response. The baseline clinical characteristics for patients withavailable spectra from pretreatment samples are listed in table 1.(Lactate dehydrogenase (LDH) is known to have prognostic significanceacross many cancer types and is used frequently as an important factorin assessing prognosis for patients with melanoma.)

TABLE 1 Baseline characteristics of patients with available spectra N(%) Gender Male 72 (61) Female 45 (38) NA 2 (2) Age Median (Range) 61(16-87) Response* CR 0 (0) PR 31 (26) SD 18 (15) PD 70 (59) TTP Median(days) 160 OS Median (weeks) 94 Prior Ipi No 31 (26) Yes 88 (74) Cohort1 9 (8) 2 11 (9)  3 11 (9)  4 10 (8)  5 21 (18) 6 57 (48) PD-L1expression Positive 8 (7) (5% tumor) Negative 29 (24) NA 82 (69) PD-L1expression Positive 18 (15) (1% tumor) Negative 19 (16) NA 82 (69) PD-L1expression Positive 28 (24) (1% tumor + immune Negative 7 (6) cells) NA84 (71) Veri Strat-like Good 98 (82) classification  

  Poor 21 (18) LDH level ^(x) (IU/L) Median (Range) 486 (149-4914) >ULN^(x x) 100 (85)  >2 ULN 31 (26) *subject to further data review;  

  see details in Appendix D of our prior provisional application, ^(x)Not available for one patient, ^(x x) ULN = upper limit of normal range

Kaplan-Meier plots for time-to-progression (TTP) and overall survival(OS) for the cohort of 119 patients with baseline samples and acquiredspectra from pretreatment samples are shown in FIGS. 1A and 1B,respectively. Note: Of the 14 patients on the plateau of the TTPKaplan-Meier plot, 3 (21%) had an objective response of SD rather thanPR.

FIGS. 2A and 2B illustrate Kaplan-Meier plots of time-to-event data forall 119 patients with available clinical data and spectra frompretreatment samples by prior treatment (no prior ipilimumab, i.e.,“Ipi-naïve”; prior ipilimumab). Differences in outcome were notstatistically significant.

FIGS. 3A and 3B are Kaplan-Meier plots of time-to-event data (TTP andOS), respectively, for all 119 patients with available clinical data andspectra from pretreatment samples showing particularly good outcomes forpatients in cohort 5, both in absolute terms and as compared to theipilimumab-naïve patients and other patients with prior ipilimumabtreatment. Recall that Cohort 5 in the nivolumab study involved patientsthat had progressed after or on prior ipilimumab therapy, and enrolledonly patients who had experienced grade 3 dose-limiting toxicities ontheir prior ipilimumab therapy.

The relatively large number of samples (119) we obtained from the studyallowed for a split of the samples into a development set and aninternal validation set for classifier development. Two different splitswere studied. The first (referred to in the following discussion as“DEV1”) was stratified by VeriStrat-like classification, response,censoring of TTP and TTP. The second (referred in to the followingdiscussion as “DEV2”), was stratified by cohort, VeriStrat-likeclassification, response, censoring of TTP and TTP. (By “VeriStrat-likeclassification” we mean assignment of a “Good” or “Poor” class label forthe mass spectra using the classification algorithm and training set forthe VeriStrat test described in U.S. Pat. No. 7,736,905). The assignmentof individual samples to either the validation set or the developmentset is listed in Appendix E of our prior provisional application Ser.No. 62/289,587. Clinical characteristics are listed for the developmentand validation split in Tables 2A and 2B and comparison of thetime-to-event data between development and validation sets is shown inFIGS. 4A-4D.

TABLE 2A Baseline characteristics of patients with available spectrasplit into development and internal validation sets (“DEV1”) DevelopmentSet (N = 60) Validation Set n(%) (N = 59) n(%) Gender Male 35 (58) 37(63) Female 25 (42) 20 (34) NA 0 (0) 2 (3) Age Median (Range) 61 (23-86)61 (16-87) Response CR 0 (0) 0 (0) PR 16 (27) 16 (27) SD  9 (15)  8 (14)PD 35 (58) 35 (59) TTP Median (days) 162 154 OS Median (weeks) 94 86Cohort 1  6 (10) 3 (5) 2 5 (8)  6 (10) 3  8 (13) 3 (5) 4  6 (10) 4 (7) 510 (17) 11 (19) 6 25 (42) 32 (54) Prior ipi yes 41 (68) 47 (80) no 19(32) 12 (20) VS-like good 10 (17) 11 (19) classification poor 50 (83) 48(81)

TABLE 2B Baseline characteristics of patients with available spectrasplit into development and internal validation sets (“DEV2”) DevelopmentSet (N = 60) Validation Set n(%) (N = 59) n(%) Gender Male 36 (60) 36(61) Female 23 (38) 22 (37) NA 1 (2) 1 (2) Age Median (Range) 60 (16-87)62 (34-85) Response CR 0 (0) 0 (0) PR 16 (27) 15 (25) SD  9 (15)  9 (15)PD 35 (58) 35 (59) TTP Median (days) 162 132 OS Median (weeks) 94 89Cohort 1 4 (7) 5 (8) 2  6 (10) 5 (8) 3  6 (10) 5 (8) 4 4 (7)  6 (10) 511 (18) 10 (17) 6 29 (48) 28 (47) Prior ipi yes 44 (73) 44 (75) no 16(27) 15 (25) VS-like good 11 (18) 10 (17) classification poor 49 (82) 49(83)

Kaplan-Meier plots for time-to-progression (TTP) and overall survival(OS) for development and validation sets are shown in FIGS. 4A, 4B, 4Cand 4D. In particular, FIGS. 4A and 4B show the time-to-event data forall 119 patients with available clinical data and spectra frompretreatment samples split into development (N=60) and validation (N=59)sets for the first split (“DEV1”); FIGS. 4C and 4D shows thetime-to-event data for all 119 patients with available clinical data andspectra from pretreatment samples split into development (N=60) andvalidation (N=59) sets for the second split (“DEV2”).

Sample Preparation

Serum samples were thawed and 3 μl aliquots of each test sample (frompatients treated with nivolumab) and quality control serum (a pooledsample obtained from serum of five healthy patients, purchased fromProMedDx, “SerumP3”) were spotted onto VeriStrat™ cellulose serum cards(Therapak). The cards were allowed to dry for 1 hour at ambienttemperature after which the whole serum spot was punched out with a 6 mmskin biopsy punch (Acuderm). Each punch was placed in a centrifugalfilter with 0.45 μm nylon membrane (VWR). One hundred μl of HPLC gradewater (JT Baker) was added to the centrifugal filter containing thepunch. The punches were vortexed gently for 10 minutes then spun down at14,000 rcf for two minutes. The flow-through was removed and transferredback on to the punch for a second round of extraction. For the secondround of extraction, the punches were vortexed gently for three minutesthen spun down at 14,000 rcf for two minutes. Twenty microliters of thefiltrate from each sample was then transferred to a 0.5 ml eppendorftube for MALDI analysis.

All subsequent sample preparation steps were carried out in a customdesigned humidity and temperature control chamber (Coy Laboratory). Thetemperature was set to 30° C. and the relative humidity at 10%.

An equal volume of freshly prepared matrix (25 mg of sinapinic acid per1 ml of 50% acetonitrile: 50% water plus 0.1% TFA) was added to each 20μl serum extract and the mix vortexed for 30 sec. The first threealiquots (2×2 μl) of sample:matrix mix were discarded into the tube cap.Eight aliquots of 2 μl sample:matrix mix were then spotted onto astainless steel MALDI target plate (SimulTOF). The MALDI target wasallowed to dry in the chamber before placement in the MALDI massspectrometer.

This set of samples was processed for MALDI analysis in three batches.QC samples were added to the beginning (two preparations) and end (twopreparations) of each batch run.

Spectral Acquisition

MALDI spectra were obtained using a MALDI-TOF mass spectrometer(SimulTOF 100 s/n: LinearBipolar 11.1024.01 from Virgin Instruments,Sudbury, Mass., USA). The instrument was set to operate in positive ionmode, with ions generated using a 349 nm, diode-pumped,frequency-tripled Nd:YLF laser operated at a laser repetition rate of0.5 kHz. External calibration was performed using a mixture of standardproteins (Bruker Daltonics, Germany) consisting of insulin (m/z 5734.51Da), ubiquitin (m/z, 8565.76 Da), cytochrome C (m/z 12360.97 Da), andmyoglobin (m/z 16952.30 Da).

Spectra from each MALDI spot (8 spots per sample) were collected as 800shot spectra that were ‘hardware averaged’ as the laser firescontinuously across the spot while the stage is moving at a speed of0.25 mm/sec. A minimum intensity threshold of 0.01 V was used to discardany ‘flat line’ spectra. All 800 shot spectra with intensity above thisthreshold were acquired without any further processing.

MALDI-TOF mass spectral data acquisition and processing (both forpurposes of acquiring a set of data for classifier development and toperform a test on a sample for patient benefit) is optionally performedin accordance with the so-called “Deep MALDI” method described in U.S.Pat. No. 9,279,798 of H. Röder et al., the content of which isincorporated by reference herein. This ‘'798 patent describes thesurprising discovery that collecting and averaging large numbers oflaser shots (typically 100,000 to 500,000 or more) from the same MALDIspot or from the combination of accumulated spectra from multiple spotsof the same sample, leads to a reduction in the relative level of noisevs. signal and that a significant amount of additional spectralinformation from mass spectrometry of complex biological samples isrevealed. The document also demonstrates that it is possible to runhundreds of thousands of shots on a single spot before the proteincontent on the spot is completely depleted. Second, the reduction ofnoise via averaging many shots leads to the appearance of previouslyinvisible peaks (i.e., peaks not apparent at spectra resulting fromtypical 1,000 laser shots). Even previously visible peaks become betterdefined and this allows for more reliable measurements of peak intensityand comparisons between samples when the sample is subject to a verylarge number of shots. The classifier of this disclosure takes advantageof the deep MALDI method to look deep into the proteome of serum samplesand uses relatively large numbers of peaks (hundreds) for classificationwhich would not be otherwise observable in conventional “dilute andshoot” spectra obtained from the typical ˜1000 shot mass spectrum. Seee.g. the definition of classification feature values listed in AppendixA.

The following section of this document describes the spectral processingwe used on the raw spectra from the mass spectrometer in order toconstruct a feature table for use in classifier generation. Thefollowing procedures are executed in software in a general purposecomputer which receives the spectra from the mass spectrometer. Some ofthe steps, such as for example defining the features used forclassification, may be performed in part or in whole by a human operatorby inspection of plots of the mass spectral data.

Spectral Processing

Raster Spectra Preprocessing

Re Scaling

Instrument calibration can introduce dramatic drifts in the location ofpeaks (mass (m)/charge (z)=m/z), most apparent in the high mass region,by batch. This results in an inability to consistently use predefinedworkflows to process the data that rely on the position of peaks and aset tolerance for alignment. To overcome the problem, rescaling of them/z data can be performed requiring a standard reference spectrum. Thestandard is compared to spectra from the current batch to identify ifthere is a shift in the position of common serum peaks. The m/z positionis borrowed from the reference and any ‘shift’ applied to rescale thespectra. The results are rescaled spectra with comparable m/z acrossbatches. In a sense, this is a batch correction procedure for grossalignment issues.

Alignment and Filtering

This workflow performs the ripple filter as it was observed that theresulting averages were improved in terms of noise. The spectra are thenbackground subtracted and peaks are found in order to perform alignment.The spectra that are used in averaging are the aligned ripple filteredspectra without any other preprocessing. The calibration step uses a setof 43 alignment points listed below in table 3. Additional filteringparameters required that the spectra have at least 20 peaks and used atleast 5 of the alignment points.

TABLE 3 Alignment points used to align the raster spectra m/z 3168 41534183 4792 5773 5802 6433 6631 7202 7563 7614 7934 8034 8206 8684 88128919 8994 9133 9310 9427 10739 10938 11527 12173 12572 12864 13555 1376313882 14040 14405 15127 15263 15869 17253 18630 21066 23024 28090 2829833500 67150

Raster Averaging

Averages were created from the pool of rescaled, aligned, and filteredraster spectra. A random selection of 500 spectra was averaged to createa final sample spectrum of 400,000 shots. We collected multiple 800 shotspectra per spot, so that we end up with a pool in excess of 500 innumber of 800 shot raster spectra from the 8 spots from each sample. Werandomly select 500 from this pool, which we average together to a final400,000 shot average deep MALDI spectrum.

Deep MALDI Average Spectra Preprocessing

Background Estimation and Subtraction

Estimation of background was performed with additional consideration forthe high mass region. The two window method of background estimation andsubtraction was used (table 4).

TABLE 4 Background estimation windows m/Z width Wide windows 3000 8000030000 80000 31000 160000 Medium 3000 5000 windows 30000 5000 31000 10000Details on background subtraction of mass spectra are known in the artand described in prior U.S. Pat. No. 7,736,905, such description ishereby incorporated by reference.

Normalization by Bin Method

A bin method was used to compare clinical groups of interest to ensurethat normalization windows are not selected that are useful forclassification. The feature definitions used in this analysis areincluded in Appendix C of our prior provisional application Ser. No.62/289,587. This method compares feature values by clinical group andcalculates the coefficient of variance (CV) of the feature for allsamples. A threshold is set for p value and for the CV to remove anyregion that significantly distinguishes the groups of interest or hasintrinsic instability (high CV). We used the clinical group comparisonsProgressive Disease (PD) vs Stable Disease (SD) and Partial Response(PR), i.e., disease control (DC) no or yes, to calculate univariate pvalues. A second comparison was added that used PR vs PD and SD. Forboth, the p value cutoff was set to 0.22. Feature bins with p valuesless than 0.22 were not included to calculate the normalization scalars.The CV cutoff that was used was 1.0. Features that had CVs above 1.0were also excluded. By hand, features above 25 kDa were removed andfeatures known to be intrinsically unstable (17 kDa region) were alsoremoved. A total of 16 bins were identified to include as normalizationwindows (see table 5).

TABLE 5 Iteration # 1 normalization bins Left Center Right 3530.6793657.668 3784.658 3785.029 3931.884 4078.739 4220.21 4271.637 4323.0654875.581 4909.742 4943.903 5260.635 5348.079 5435.524 5436.47 5559.4515682.433 6050.421 6213.614 6376.807 6510.852 6555.966 6601.081 7751.4147825.12 7898.826 10606.12 10751.66 10897.2 10908.61 11132.56 11356.5112425.27 12476.27 12527.26 17710.35 18107.52 18504.69 19212.92 19978.3720743.82 22108.95 22534.05 22959.15 23738.5 24238.77 24739.04A second iteration of normalization by bin was performed on the spectranormalized using iteration #1 bins. The CVs following the firstiteration were in general lower. This allowed a new threshold for CV of0.68 to be set. The p value cutoff was increased to add stringency tothe requirements. The same clinical groups were used in the evaluation.The second iteration resulted in 9 windows for inclusion asnormalization bins (see table 6).

TABLE 6 Iteration # 2 normalization bins Left Center Right 4168.2264194.033 4219.839 4875.581 4909.742 4943.903 4946.131 5011.854 5077.5765080.918 5170.405 5259.892 5260.635 5348.079 5435.524 6510.852 6555.9666601.081 7751.414 7825.12 7898.826 10606.12 10751.66 10897.2 10908.6111132.56 11356.51

The resulting scalars using these windows were found for each spectrumand were compared by disease control groups, i.e., scalars for spectrafrom patients with disease control were compared with spectra forpatients with no disease control to ensure that there were nosignificant differences in scalars depending on clinical group. The plotof normalization scalars shown in FIG. 5 reveals that the distributionof the resulting scalars was not significantly different between theclinical groups, and thus the normalization bins were not useful forclassification. The spectra were normalized using partial ion current(PIC) over these windows.

Average Spectra Alignment

The peak alignment of the average spectra is typically very good;however, a fine-tune alignment step was performed to address minordifferences in peak positions in the spectra. A set of alignment pointswas identified and applied to the analysis spectra (Table 7).

TABLE 7 Alignment points used to align the spectral averages m/Z 33154153 4457 4710 5066 6433 6631 7934 8916 9423 9714 12868 13766 1404514093 15131 15872 16078 17256 17383 18631 21069 21168 28084 28293 67150

Feature Definitions

After performing all of the above pre-processing steps, the process ofclassifier development proceeded with the identification and definitionof features (m/z regions) that are useful for classification. All 119average spectra were viewed simultaneously by clinical groups PD, SD,and PR to select features for classification. This method protects thatfeatures found only in a subset of the spectra are not missed and thatfeature definitions are broad enough to cover variations of peak widthor position. A total of 351 features were defined for the dataset. SeeAppendix A. These feature definitions were applied to all spectra tocreate a feature table of feature values (integrated intensity valuesover each feature) for each of the 119 spectra. An example of selectedfeatures is shown in FIG. 6, with the shaded regions representing them/z region defined for each feature.

Batch Correction of Analysis Spectra

Reference Sample “SerumP3” Analysis

Two preparations of the reference sample, SerumP3, were plated at thebeginning (1,2) and end (3,4) of each run of samples through theMALDI-TOF mass spectrometer. The purpose of these samples is to ensurethat variations by batch due to slight changes in instrument performance(for example, aging of the detector) can be corrected for. To performbatch correction, one spectrum, which is an average of one of thepreparations from the beginning and one from the end of the batch, mustserve as the reference for the batch. The procedure used for selectingthe pair is described first.

The reference samples were preprocessed as described above. All 351features (Appendix A) were used to evaluate the possible combinations(1-3, 1-4, 2-3, 2-4). We compared each possible combination ofreplicates using the function:

A=min(abs(1−ftrval1/ftrval2),abs(1−ftrval2/ftrval1))

where ftrval1 (ftrval2) is the value of a feature for the first (second)replicate of the replicate pair. This quantity A gives a measure of howsimilar the replicates of the pair are. For each feature, A is reported.If the value is >0.5, then the feature is determined to be discordant,or ‘Bad’. A tally of the bad features is reported for each possiblecombination. If the value of A is <0.1, then the feature is determinedto be concordant and reported as ‘Good’. A tally of the Good features isreported for each possible combination. Using the tallies of Bad andGood features from each possible combination, we computed the ratio ofBad/Good. The combination with the lowest ratio was reported as the mostsimilar combination, unlikely to contain any systematic or localizedoutlier behavior in either of the reference spectra. If no ratio can befound that is less than 0.12, then the batch is declared a failure.Table 8 reports the combinations that were found most similar for eachbatch.

TABLE 8 SerumP3 preparations found to be most similar by batch BatchCombination 1 2_4 2 1_4 3 2_4

Batch Correction

Batch 1 was used as the baseline batch to correct all other batches. Thereference sample was used to find the correction coefficients for eachof the batches 2 and 3 by the following procedure.

Within each batch j (2≦j≦3), the ratio

${\hat{r}}_{i}^{j} = \frac{A_{i}^{j}}{A_{i}^{1}}$

and the average amplitude

${\overset{\_}{A}}_{i}^{j} = {\frac{1}{2}( {A_{i}^{j} + A_{i}^{1}} )}$

are defined for each i^(th) feature centered at (m/z)_(i), where A_(i)^(j) is the average reference spectra amplitude of feature i in thebatch being corrected and A_(i) ¹ is the reference spectra amplitude offeature i in batch 1 (the reference standard). It is assumed that theratio of amplitudes between two batches follows the dependence:

r( A ,(m/z))=(a ₀ +a ₁ ln( A ))+(b ₀ +b ₁ ln( A ))(m/z)+c ₀(m/z)².

On a batch to batch basis, a continuous fit is constructed by minimizingthe sum of the square residuals, Δ^(j)=Σ_(i)({circumflex over (r)}_(i)^(j)−r^(j)(a₀, a₁, b₀, b₁, c₀))², and using the experimental data of thereference sample. The SerumP3 reference samples are used to calculatethe correction function. Steps were taken to not include outlier pointsin order to avoid bias in the parameter estimates. The values of thecoefficients a₀, a₁, b₀, b₁ and c₀, obtained for the different batchesare listed in Appendix B (table B.1) of our prior provisionalapplication Ser. No. 62/289,587. The projection in the {circumflex over(r)}_(i) ^(j) versus (m/z)_(i) plane of the points used to construct thefit for each batch of reference spectra, together with the surfacedefined by the fit itself, is shown in Figure B.1 of Appendix B of ourprior provisional application Ser. No. 62/289,587.

Once the final fit, r^(j)(Ā,(m/z)), is determined for each batch, thenext step is to correct, for all the samples, all the features (withamplitude A at (m/z)) according to

$A_{corr} = {\frac{A}{r^{j}( {\overset{\_}{A},( {m/z} )} )}.}$

After this correction, the corrected (Ā_(i) ^(j), (m/z)_(i), {circumflexover (r)}_(i) ^(j)) feature values calculated for reference spectra liearound the horizontal line defined by r=1, as shown in Figure B.1 ofAppendix B of our prior provisional application Ser. No. 62/289,587.Post-correction coefficients are calculated to compare to qualitycontrol thresholds. These coefficients can be found in Appendix B tableB.2 and the corresponding plots in Figure B.2 of our prior provisionalapplication Ser. No. 62/289,587.

Using the 351 features and all SerumP3 samples from all batches, areproducibility assessment was performed on the feature values beforeand after batch correction. In summary, the median and average CVs were14.8% and 18.3% before batch correction. Following batch correction, themedian and average CVs were 15.0% and 18.2%. As seen in the plots foundin Appendix B of our prior provisional application Ser. No. 62/289,587,the batches were very similar requiring little in correction. This isreflected in the lack of improvement in the CVs by feature over allSerumP3 samples.

Partial Ion Current (PIC) Normalization

We have found it advantageous to perform a normalization of spectrabefore batch correction (see above). However, we have found that afterbatch correction we can improve the coefficient of variances (CVs) offeatures and obtain better results if we do another normalization. Thissecond PIC normalization is based on smaller windows around individualpeaks that are identified above.

The spectra were normalized using a partial ion current (PIC)normalization method. Background information on partial ion currentnormalization is described in the prior U.S. Pat. No. 7,736,905, suchdescription is incorporated by reference here. The full feature tablewas examined to find regions of intrinsic stability to use as the finalnormalization windows. First, the univariate p values were found bycomparing the DC groups by feature. Features with p values less than0.15 were excluded from the PIC analysis as these features maycontribute meaningful information to the test to be developed. A set of221 features were used in the PIC analysis, of which 30 features wereused for the final normalization (table 9).

TABLE 9 Features used for PIC normalization m/Z 3243 3265 3420 3554 36793953 4009 4409 4891 5068 5104 5403 6193 6438 6589 6612 6657 6681 67327074 8902 9020 9038 10637 12738 12786 13943 14098 14199 14255To normalize, the listed features were summed to find the normalizationfactor for each sample. All feature values were then divided by thenormalization factor to arrive at the final feature table used in thesubsequent classifier generation method of FIG. 8. The normalizationfactors were examined by DC groups to test that the calculated factorswere not significantly correlated. The plot of FIG. 7 illustrates thedistribution of the factors. The plots for the two groups are verysimilar, indicating that the normalization scalars are appropriate touse.

Classifier Development Using Diagnostic Cortex™

After the feature table for features in the mass spectra for the 119samples was created (as explained above) we proceeded to develop aclassifier using the classifier generation method shown in flow-chartform in FIG. 8. This method, known as “combination of mini-classifierswith drop-out regularization” or “CMC/D”, or DIAGNOSTIC CORTEX™, isdescribed at length in the pending U.S. patent application publicationno. 2015/0102216 of H. Röder et al., the entire content of which isincorporated by reference herein. An overview of the methodology will beprovided here first, and then illustrated in detail in conjunction withFIG. 8 for the generation of the melanoma/nivolumab classifier.

In contrast to standard applications of machine learning focusing ondeveloping classifiers when large training data sets are available, thebig data challenge, in bio-life-sciences the problem setting isdifferent. Here we have the problem that the number (n) of availablesamples, arising typically from clinical studies, is often limited, andthe number of attributes (measurements) (p) per sample usually exceedsthe number of samples. Rather than obtaining information from manyinstances, in these deep data problems one attempts to gain informationfrom a deep description of individual instances. The present methodstake advantage of this insight, and are particularly useful, as here, inproblems where p>>n.

The method includes a first step a) of obtaining measurement data forclassification from a multitude of samples, i.e., measurement datareflecting some physical property or characteristic of the samples. Thedata for each of the samples consists of a multitude of feature values,and a class label. In this example, the data takes the form of massspectrometry data, in the form of feature values (integrated peakintensity values at a multitude of m/z ranges or peaks, see Appendix A)as well as a label indicating some attribute of the sample (for example,patient Early or Late death or disease progression). In this example,the class labels were assigned by a human operator to each of thesamples after investigation of the clinical data associated with thesample. The development sample set is then split into a training set anda test set and the training set is used in the following steps b), c)and d).

The method continues with a step b) of constructing a multitude ofindividual mini-classifiers using sets of feature values from thesamples up to a pre-selected feature set sizes (s=integer 1 . . . n).For example a multiple of individual mini- or atomic classifiers couldbe constructed using a single feature (s=1), or pairs of features (s=2),or three of the features (s=3), or even higher order combinationscontaining more than 3 features. The selection of a value of s willnormally be small enough to allow the code implementing the method torun in a reasonable amount of time, but could be larger in somecircumstances or where longer code run-times are acceptable. Theselection of a value of s also may be dictated by the number ofmeasurement data values (p) in the data set, and where p is in thehundreds, thousands or even tens of thousands, swill typically be 1, or2 or possibly 3, depending on the computing resources available. Themini-classifiers execute a supervised learning classification algorithm,such as k-nearest neighbors (kNN), in which the values for a features,pairs or triplets of features of a sample instance are compared to thevalues of the same feature or features in a training set and the nearestneighbors (e.g., k=9) in an s-dimensional feature space are identifiedand by majority vote a class label is assigned to the sample instancefor each mini-classifier. In practice, there may be thousands of suchmini-classifiers depending on the number of features which are used forclassification.

The method continues with a filtering step c), namely testing theperformance, for example the accuracy, of each of the individualmini-classifiers to correctly classify the sample, or measuring theindividual mini-classifier performance by some other metric (e.g. thedifference between the Hazard Ratios (HRs) obtained between groupsdefined by the classifications of the individual mini-classifier for thetraining set samples) and retaining only those mini-classifiers whoseclassification accuracy, predictive power, or other performance metric,exceeds a pre-defined threshold to arrive at a filtered (pruned) set ofmini-classifiers. The class label resulting from the classificationoperation may be compared with the class label for the sample known inadvance if the chosen performance metric for mini-classifier filteringis classification accuracy. However, other performance metrics may beused and evaluated using the class labels resulting from theclassification operation. Only those mini-classifiers that performreasonably well under the chosen performance metric for classificationare maintained. Alternative supervised classification algorithms couldbe used, such as linear discriminants, decision trees, probabilisticclassification methods, margin-based classifiers like support vectormachines, and any other classification method that trains a classifierfrom a set of labeled training data.

To overcome the problem of being biased by some univariate featureselection method depending on subset bias, we take a large proportion ofall possible features as candidates for mini-classifiers. We thenconstruct all possible kNN classifiers using feature sets up to apre-selected size (parameter s). This gives us many “mini-classifiers”:e.g. if we start with 100 features for each sample (p=100), we would get4950 “mini-classifiers” from all different possible combinations ofpairs of these features (s=2), 161,700 mini-classifiers using allpossible combination of three features (s=3), and so forth. Othermethods of exploring the space of possible mini-classifiers and featuresdefining them are of course possible and could be used in place of thishierarchical approach. Of course, many of these “mini-classifiers” willhave poor performance, and hence in the filtering step c) we only usethose “mini-classifiers” that pass predefined criteria. These filteringcriteria are chosen dependent on the particular problem: If one has atwo-class classification problem, one would select only thosemini-classifiers whose classification accuracy exceeds a pre-definedthreshold, i.e., are predictive to some reasonable degree. Even withthis filtering of “mini-classifiers” we end up with many thousands of“mini-classifier” candidates with performance spanning the whole rangefrom borderline to decent to excellent performance.

The method continues with step d) of generating a master classifier (MC)by combining the filtered mini-classifiers using a regularizedcombination method. In one embodiment, this regularized combinationmethod takes the form of repeatedly conducting a logistic training ofthe filtered set of mini-classifiers to the class labels for thesamples. This is done by randomly selecting a small fraction of thefiltered mini-classifiers as a result of carrying out an extreme dropoutfrom the filtered set of mini-classifiers (a technique referred to asdrop-out regularization herein), and conducting logistical training onsuch selected mini-classifiers. While similar in spirit to standardclassifier combination methods (see e.g. S. Tulyakov et al., Review ofClassifier Combination Methods, Studies in Computational Intelligence,Volume 90, 2008, pp. 361-386), we have the particular problem that some“mini-classifiers” could be artificially perfect just by random chance,and hence would dominate the combinations. To avoid this overfitting toparticular dominating “mini-classifiers”, we generate many logistictraining steps by randomly selecting only a small fraction of the“mini-classifiers” for each of these logistic training steps. This is aregularization of the problem in the spirit of dropout as used in deeplearning theory. In this case, where we have many mini-classifiers and asmall training set we use extreme dropout, where in excess of 99% offiltered mini-classifiers are dropped out in each iteration.

In more detail, the result of each mini-classifier is one of two values,either “Early” or “Late” in this example. We can then use logisticregression to combine the results of the mini-classifiers in the spiritof a logistic regression by defining the probability of obtaining an“Early” label via standard logistic regression (see e.g.http://en.wikipedia.org/wiki/Logistic_regression)

$\begin{matrix}{{P( {``{Early}"} \middle| {{feature}\mspace{14mu} {for}\mspace{14mu} a\mspace{14mu} {spectrum}} )} = \frac{\exp( {\sum\limits_{{mini}\mspace{14mu} {classifiers}}{w_{m\; c}{I( {m\; {c( {{feature}\mspace{14mu} {values}} )}} )}}} )}{Normalization}} & {{Eq}.\mspace{14mu} (1)}\end{matrix}$

where I(mc(feature values))=1, if the mini-classifier mc applied to thefeature values of a sample returns “Early”, and 0 if the mini-classifierreturns “Late”. The weights w_(mc) for the mini-classifiers are unknownand need to be determined from a regression fit of the above formula forall samples in the training set using +1 for the left hand side of theformula for the Late-labeled samples in the training set, and 0 for theEarly-labeled samples, respectively. As we have many moremini-classifiers, and therefore weights, than samples, typicallythousands of mini-classifiers and only tens of samples, such a fit willalways lead to nearly perfect classification, and can easily bedominated by a mini-classifier that, possibly by random chance, fits theparticular problem very well. We do not want our final test to bedominated by a single special mini-classifier which only performs wellon this particular set and is unable to generalize well. Hence wedesigned a method to regularize such behavior: Instead of one overallregression to fit all the weights for all mini-classifiers to thetraining data at the same time, we use only a few of themini-classifiers for a regression, but repeat this process many times ingenerating the master classifier. For example we randomly pick three ofthe mini-classifiers, perform a regression for their three weights, pickanother set of three mini-classifiers, and determine their weights, andrepeat this process many times, generating many random picks, i.e.realizations of three mini-classifiers. The final weights defining themaster classifier are then the averages of the weights over all suchrealizations. The number of realizations should be large enough thateach mini-classifier is very likely to be picked at least once duringthe entire process. This approach is similar in spirit to “drop-out”regularization, a method used in the deep learning community to addnoise to neural network training to avoid being trapped in local minimaof the objective function.

Other methods for performing the regularized combination method in step(d) that could be used include:

-   -   Logistic regression with a penalty function like ridge        regression (based on Tikhonov regularization, Tikhonov, Andrey        Nikolayevich (1943). “        ” [On the stability of inverse problems]. Doklady Akademii Nauk        SSSR 39 (5): 195-198.)    -   The Lasso method (Tibshirani, R. (1996). Regression shrinkage        and selection via the lasso. J. Royal. Statist. Soc B., Vol. 58,        No. 1, pages 267-288).    -   Neural networks regularized by drop-out (Nitish Shrivastava,        “Improving Neural Networks with Dropout”, Master's Thesis,        Graduate Department of Computer Science, University of Toronto),        available from the website of the University of Toronto Computer        Science department.    -   General regularized neural networks (Girosi F. et al, Neural        Computation, (7), 219 (1995)).

The above-cited publications are incorporated by reference herein. Ourapproach of using drop-out regularization has shown promise in avoidingover-fitting, and increasing the likelihood of generating generalizabletests, i.e. tests that can be validated in independent sample sets. Theperformance of the master classifier is then evaluated by how well itclassifies the subset of samples forming the test set.

In step e), steps b)-d) are repeated in the programmed computer fordifferent realizations of the separation of the set of samples into testand training sets, thereby generating a plurality of master classifiers,one for each realization of the separation of the set of samples intotraining and test sets. The performance of the classifier is evaluatedfor all the realizations of the separation of the development set ofsamples into training and test sets. If there are some samples whichpersistently misclassify when in the test set, the process optionallyloops back and steps b), c) and d) and e) are repeated with flippedclass labels for such misclassified samples.

The method continues with step f) of defining a final classifier fromone or a combination of more than one of the plurality of masterclassifiers. In the present example, the final classifier is defined asa majority vote of all the master classifiers resulting from eachseparation of the sample set into training and test sets, oralternatively by an average probability cutoff

Turning now to FIG. 8, the classifier development process will bedescribed in further detail in the context of the melanoma/nivolumabclassifier.

The set of 119 samples we had available was initially randomly dividedinto two subsets, a set of 59 samples to be used for validation of theclassifier we generated, and a development set (100) of the remaining 60samples. This split was performed twice (see the discussion of DEV1 andDEV2 above) with stratification as described previously.

At step 102, a definition of the two class labels (or groups) for thesamples in the development set 100 was performed. While some preliminaryapproaches used for classifier development employed well-defined classlabels, such as response categories, these proved to be unsuccessful.All approaches discussed in this application make use of time-to-eventdata for classifier training. In this situation, the initial class labeldefinition (Early, Late) is not obvious and, as shown in FIG. 8, theprocess uses an iterative method to refine class labels at the same timeas creating the classifier (see loop 146 discussed below). At thebeginning, an initial guess is made for the class labels. Typically, thesamples are sorted on either TTP or OS and half of the samples with thelowest time-to-event outcome are assigned the “Early” class label (earlydeath or progression, i.e. poor outcome) while the other half areassigned the “Late” class label (late death or progression, i.e. goodoutcome). A classifier is then constructed using the outcome data andthese class labels. This classifier can then be used to generateclassifications for all of the development set samples and these arethen used as the new class labels for a second iteration of theclassifier construction step. This process is iterated untilconvergence.

While one could define “Early” and Late” by setting a cutoff based onclinical data, we started with an initial assignment on training labelsusing TTP (time to progression) data, with 30 patients with lowest TTPassigned the class label “Early”, and the 30 patients with highest TTPassigned the class label “Late”. It will be noted that later in theprocess we have a procedure for flipping class labels for samples whichpersistently misclassify, so these initial class label assignments arenot necessarily fixed. After this initial class label definition isarrived at the samples are then assigned to the Early and Late classesbased on outcome data as indicated by the two groups 104 and 106 in FIG.8.

At step 108, the Early and Late samples of the development set (100) arethen divided randomly into training (112) and test sets (110), 30patients each. This division is performed in a stratified manner. Twentysamples from each class were generally assigned to the training set andthe remainder to the test set. Occasionally, during the class label fliprefinement process, the number of samples in the Early group dropped toolow to allow 20 samples to be assigned to the training set and stillhave a reasonable number of samples (e.g. more than 7 or 8) in the testset. In these cases, a smaller number of samples, for example 17 or 18,were assigned to the training set from each class. In all cases thenumber of samples assigned to training from each class was the same. Thetraining set (112) is then subject to steps 120, 126 and 130. In step120, many k-nearest neighbor (kNN) mini-classifiers (mCs) that use thetraining set as their reference set are constructed (defined) usingsubsets of features from the 351 mass spectral features identified (seeAppendix A). For many of the investigations we performed, all possiblesingle features and pairs of features were examined (s=2); however, whenfewer features were used, triplets were also sometimes considered (s=3).Although different values of k in the kNN algorithm were tried inpreliminary investigations, the approaches described in Example 1 alluse k=9. To be able to consider subsets of single, two, or threefeatures and improve classifier performance, it was necessary todeselect features that were not useful for classification from the setof 351 features. This was done using the bagged feature selectionapproach outlined in Appendix F of our prior provisional applicationSer. No. 62/289,587. Further details on this methodology, its rationaleand benefits, are described in the pending U.S. patent application of J.Roder et al., Ser. No. 15/091,417 filed Apr. 5, 2016 and in U.S.provisional application Ser. No. 62/319,958 filed Apr. 8, 2016, thecontent of which is incorporated by reference herein. A reduced,selected list of features for different approaches for classification islisted in Appendix B.

In step 126 a filtering process was used to select only thosemini-classifiers (mC) that had useful or good performancecharacteristics. This can be understood in FIG. 8 by the spectra 124containing many individual features (shown by the hatched regions) andthe features alone and in pairs are indicated in the feature space 122.For some of the kNN mini-classifiers, the features (singly or in pairs)perform well for classification of the samples and such mini-classifiersare retained (indicated by the “+” sign in FIG. 8 at 128) whereas othersindicated by the “−” sign are not retained.

To target a final classifier that has certain performancecharacteristics, these mCs were filtered as follows. Each mC is appliedto its training set and performance metrics are calculated from theresulting classifications of the training set. Only mCs that satisfythresholds on these performance metrics pass filtering to be usedfurther in the process. The mCs that fail filtering are discarded. Forthis project, accuracy filtering and hazard ratio filtering were used.Sometimes, a simple single filter was used, and sometimes a compoundfilter was constructed by combining multiple single filters with alogical “AND” operation. For accuracy filtering, the classifier wasapplied to the training set of samples, or a subset of the training setof samples, and the accuracy of the resulting classification had to liewithin a preset range for the mC to pass filtering. For hazard ratiofiltering, the classifier was applied to the training set, or a subsetthereof. The hazard ratio for a specified outcome (TTP or OS) was thencalculated between the group classified as Early and the rest classifiedas Late. The hazard ratio had to lie within specified bounds for the mCto pass filtering. In this particular classifier exercise, we filteredthe mini-classifiers by Hazard ratio (HR) between early/late, and byaccuracy for early class label (with TTP defined as <100 days) and late(with TTP defined as >365 days).

TABLE 10 Parameters used in mini-classifiers and filtering Depth s (max# features Development/Validation Approach per mC) Split Filter 1 3 DEV1HR on TTP and classification accuracy on TTP <100 days and TTP >365 days2 2 DEV1 HR on OS 3 2 DEV2 HR on TTP and classification accuracy on TTP<100 days and TTP >365 days 4 2 DEV2 HR on OSHere, “approach” means different classifier development exercises. Inessence, the process of FIG. 8 was repeated a number of different times,with each iteration a different value of the depth parameter s was usedand different mini-classifier filtering criteria were used in step 126of FIG. 8 as indicated by Table 10. Note that this exercise was donetwice for each of the two separations of the entire set of 119 samplesinto development and validation sets (DEV1 and DEV 2).

At step 130, we generated a master classifier (MC) for each realizationof the separation of the development set into training and test sets atstep 108. Once the filtering of the mCs was complete, at step 132 themCs were combined in one master classifier (MC) using a logisticregression trained using the training set class labels, step 132. Seethe previous discussion of drop-out regularization. To help avoidoverfitting the regression is regularized using extreme drop out withonly a small number of the mCs chosen randomly for inclusion in each ofthe logistic regression iterations. The number of dropout iterations wasselected based on the typical number of mCs passing filtering to ensurethat each mC was likely to be included within the drop out processmultiple times. All approaches outlined in Example 1 left in 10 randomlyselected mCs per drop out iteration. Approaches using only single andpairs of features used 10,000 drop out iterations; approaches usingsingle, pairs and triplets of features used 100,000 drop out iterations.

At step 134, we evaluated the performance of the MC arrived at in step132 and its ability to classify the test set of samples (110). With eachiteration of step 120, 126, 130, 134 we evaluate the performance of theresulting MC on its ability to classify the members of the test set 110.

After the evaluation step 134, the process looped back via loop 135 tostep 108 and the generation of a different realization of the separationof the development set into training and test sets. The process of steps108, 120, 126, 130, 132, 134 and looping back at 135 to a new separationof the development set into training and test sets (step 108) wasperformed many times, and in this project six hundred and twenty fivedifferent realizations (loops) were used. The methodology of FIG. 8works best when the training set classes have the same number ofsamples. Hence, if classes had different numbers of members, they weresplit in different ratios into test and training. The use of multipletraining/test splits (loop 135 and the subsequent performance of steps120, 126, 130, 132 and 134) avoids selection of a single, particularlyadvantageous or difficult, training set split for classifier creationand avoids bias in performance assessment from testing on a test setthat could be especially easy or difficult to classify.

At step 136, there is an optional procedure of analyzing the data fromthe training and test splits, and as shown by block 138 obtaining theperformance characteristics of the MCs from each training/test set splitand their classification results. Optional steps 136 and 138 were notperformed in this project.

At step 144, we determine if there are samples which are persistentlymisclassified when they are present in the test set 110 during the manyiterations of loop 135. If so, we flip the class label of suchmisclassified samples and loop back in loop 146 to the beginning of theprocess at step 102 and repeat the methodology shown in FIG. 8.

If at step 144 we do not have samples that persistently misclassify, wethen proceed to step 150 and define a final classifier in one of severalways, including (i) a majority vote of each master classifier (MC) foreach of the realizations of the separation of the development set intotraining and test sets, or (ii) an average probability cutoff. Theoutput of the logistic regression that defines each MC (step 132) is aprobability of being in one of the two training classes (Early or Late).These MC probabilities can be averaged to yield one average probabilityfor a sample. When working with the development set (100), this approachis adjusted to average over MCs for which a given sample is not includedin the training set (“out-of-bag” estimate). These average probabilitiescan be converted into a binary classification by applying a threshold(cutoff). During the iterative classifier construction and labelrefinement process, classifications were assigned by majority vote ofthe individual MC labels obtained with a cutoff of 0.5. This process wasmodified to incorporate only MCs where the sample was not in thetraining set for samples in the development set (modified, or“out-of-bag” majority vote). This procedure gives very similarclassifications to using a cutoff of 0.5 on the average probabilitiesacross MCs.

After the final classifier is defined at step 150, the processoptionally continues with a validation step 152 in which the finalclassifier defined at step 150 is tested on an internal validation setof samples, if it is available. In the present example, the initial setof 119 samples was divided into development set (100) and a separateinternal validation set, and so this validation set existed and wassubject to the validation step 152. Ideally, in step 154 this finalclassifier as defined at step 150 is also validated on an independentsample set. We describe the validation on an independent set of sampleslater in this Example 1.

Results of Example 1 Classifier

The goal of this exercise was to determine if the final classifierdefined in accordance with FIG. 8 could demonstrate a separation betweenthe Early and Late class labels, in other words, predict from a massspectrum of a pre-treatment serum sample whether the patient providingthe sample was likely to obtain benefit from administration of nivolumabin treatment of cancer. We achieved this goal, as demonstrated by theclassifier performance data in this section of the document. Theperformance of the classifiers was assessed using Kaplan-Meier plots ofTTP and OS between samples classified as Early and Late, together withcorresponding hazard ratios (HRs) and log-rank p values. The results forthe four approaches of table 10 are summarized in table 11. The tablelists the classifier performance for both the Development Set (100 inFIG. 8), and the other half of the set of 119 samples, i.e., theValidation Set. Note in table lithe substantial difference in median OSand TTP between the Early and Late groups as identified by theclassifier developed in FIG. 8, indicating the ability of the classifierto identify clinically useful groups.

TABLE 11 Final Classifier performance summary for the four approaches oftable 10 TTP log- OS HR OS log- OS Median TTP HR rank TTP Medianapproach #Early/#Late (95% CI) rank p (Early, Late) (95% CI) p (Early,Late) Development Set 1 26/34 0.38 0.006 67, not 0.55 0.040 83, 254(0.17-0.73) reached (weeks) (0.28-0.96) (days) 2 26/34 0.30 0.001 65,not 0.53 0.032 83, 317 (0.13-0.57) reached (weeks) (0.27-0.94) (days) 325/35 0.32 0.001 61, not 0.43 0.003 82, 490 (0.13-0.58) reached (weeks)(0.19-0.71) (days) 4 28/32 0.36 0.005 73, not 0.48 0.013 83, 490(0.16-0.71) reached (weeks) (0.24-0.84) (days) Validation Set 1 23/360.47 0.029 60, 101 0.51 0.021 132, 181 (0.20-0.91) (weeks) (0.25-0.88)(days) 2 25/34 0.51 0.053 73, 101 0.46 0.006 90, 273 (0.23-1.01) (weeks)(0.22-0.76) (days) 3 16/43 0.49 0.040 55, 99 0.50 0.021 86, 179(0.18-0.95) (weeks) (0.20-0.86) (days) 4 18/41 0.43 0.013 55, 101 0.480.012 87, 183 (0.16-0.80) (weeks) (0.20-0.80) (days)Kaplan-Meier plots corresponding to the data in table 11 are shown inFIGS. 9-12 for the four approaches. The classifications per sample foreach approach are listed in Appendix G of our prior provisionalapplication Ser. No. 62/289,587. Baseline clinical characteristics aresummarized by classification group for each of the four approaches intable 12.

TABLE 12 Clinical characteristic by classification group Approach 1Approach 2 Approach 3 Approach 4 Early Late Early Late Early Late EarlyLate (N = 49) (N = 70) (N = 51) (N = 68) (N = 41) (N = 78) (N = 46) (N =73) Gender Male 30 42 31 41 23 49 27 45 Female 17 28 18 27 16 29 17 28Age Median 63 60 61 61 63 60 62 60 (Range) (23-86) (16-87) (23-86)(16-87) (23-86) (16-87) (23-86) (16-87) Response PR  9 22  8 23  6 25  724 SD  4 14  4 14  3 15  4 14 PD 36 34 39 31 32 38 35 35 Cohort 1  3  6 3  6  2  7  2  7 2  5  6  5  6  5  6  5  6 3  2  9  3  8  2  9  3  8 4 7  3  7  3  6  4  7  3 5  6 15  6 15  5 16  6 15 6 26 31 27 30 21 36 2334 Prior Ipi Yes 10 21 11 20  9 22 10 21 No 39 49 40 48 32 56 36 52VS-like good 28 70 30 68 20 78 25 73 classification poor 21  0 21  0 21 0 21  0 PD-L1 Positive  3  5 expression Negative 14 15 (5% tumor) NA 3250 PD-L1 Positive  8 10 expression Negative  9 10 (1% tumor) NA 32 50PD-L1 Positive 12 16 expression Negative  4  3 (1% tumor/ NA 33 51immune cells)The data in tables 11-12 and FIGS. 9-12 demonstrate that it is possibleto build classifiers able to identify patients with better and worseoutcomes on nivolumab therapy from mass spectra generated frompre-treatment serum samples. Patients classified as Late have better OSand TTP than patients classified as Early. In addition, patientsclassified as Late are more likely than patients classified as Early tohave a partial response to or stable disease on nivolumab therapy. Table12 illustrates that the proportions of patients PD-L1 positive issimilar in Early and Late classification groups, so the two quantitiesare not significantly correlated and the serum classifier of Example 1would provide additional information to any provided by PD-L1 expressionlevels.

These results shows a consistency in the ability to identify patientswith better and worse outcomes on nivolumab therapy acrossdevelopment/validation set splits, with good generalization betweenvalidation and development set performance. Having demonstrated thatthere is an appreciable, validating performance, to create the mostrobust classifier, the two mini-classifier filtering methods used wereapplied to the whole set of 119 samples as the development set 100 ofFIG. 8. The results are summarized in Table 13 and the Kaplan-Meierplots are shown in FIG. 13. (A classifier constructed in accordance withFIG. 8 using all 119 samples in the development set as explained hereand in Table 13 is referred to as “the full-set classifier of Example 1”later in this document.)

TABLE 13 Performance of the two classifiers built on the whole set of119 samples as development set OS Median TTP TTP #Early/ OS HR OS log-(Early, TTP HR log- Median Approach #Late (95% CI) rank p Late) (95% CI)rank p (Early, Late) 2 47/72 0.42 <0.001 61, 113 0.55 0.004 85, 200(compound (0.22-0.63) (weeks) (0.33-0.80) (days) TTP filtering) 1 47/720.38 <0.001 61, not 0.50 0.001 84, 230 (simple OS (0.19-0.55) reached(0.29-0.71) (days) filtering) (weeks)These two approaches show very similar performance, both being veryconsistent with the classifiers built on a development/validation splitof the 119 samples. This is a further indication that the procedure ofFIG. 8 produces reliable classifiers with good generalization.

Clinical characteristics are summarized by the classification groupsgiven by approach 1 (simple OS filtering) in Table 14. There is no hintof association of Early and Late classification with PD-L1 expressionwith any cutoff or with gender or age. More than 70% of patients incohort 5 are classified as Late, while patients in cohorts 4 and 6 aresplit roughly evenly between the two classification groups. LDH issignificantly higher in the Early group than in the Late group(Mann-Whitney p value=0.003).

TABLE 14 Baseline characteristics by classification group for thefull-set classifier approach 1 Early (N = 47) Late (N = 72) Gender Male28 (60) 44 (61) Female 17 (36) 28 (39) Age Median 61 (23-86) 60 (16-87)(Range) Response PR  7 (15) 24 (33) SD 4 (9) 14 (19) PD 36 (77) 34 (47)Cohort 1 2 (4)  7 (10) 2  5 (11) 6 (8) 3 3 (6)  8 (11) 4  7 (15) 3 (4) 5 6 (13) 15 (21) 6 24 (51) 33 (46) Prior Ipi Yes 10 (21) 21 (29) No 37(79) 51 (71) VS-like good 26 (55)  72 (100) classification poor 21 (45)0 (0) PD-L1 Positive 3 (6) 5 (7) expression Negative 15 (32) 14 (19) (5%tumor) NA 29 (62) 53 (74) PD-L1 Positive  9 (19)  9 (13) expressionNegative  9 (19) 10 (14) (1% tumor) NA 29 (62) 53 (74) PD-L1 Positive 13(28) 15 (21) expression Negative 4 (9) 3 (4) (1% NA 30 (64) 54 (75)tumor/immune cells) LDH level ^(x) Median 655 (417-1130) 469 (351-583)(IU/L) (Range) >ULN ^(x x) 43 (91) 57 (80) >2 ULN 23 (49)  8 (11) ^(x)Missing for one patient, ^(x x) ULN = upper limit of normal range

Multivariate analysis shows that Early/Late classification remainsindependently significant when adjusted for other clinical factors.

TABLE 15 Multivariate analysis of OS and TTP OS TTP Covariate HR (95%CI) P value HR (95% CI) P value Late vs Early 2.86 (1.69- <0.001 2.22(1.41- <0.001 5.00) 3.45) Male vs Female 1.66 (0.96- 0.069 1.73 (1.09-0.020 2.88) 2.75) Prior Ipi (no vs yes) 0.63 (0.35- 0.112 0.70 (0.42-0.168 1.12) 1.16) PD-L1 (5%) −ve/NA vs 0.81 (0.27- 0.704 1.25 (0.53-0.613 +ve 2.41) 2.98) PD-L1 (5%) −ve/+ve vs 1.00 (0.55- 0.987 1.18(0.69- 0.546 NA 1.81) 2.03) LDH (IU/L)/1000 1.77 (1.26- <0.001 1.59(1.16- 0.004 2.48) 2.17)This table shows that the serum test adds information additional to theother available clinical characteristics and that the serumclassification label remains a significant predictor of both TTP and OSeven when adjusting for other available characteristics, including PD-L1expression level and LDH level. In particular, it is of note that eventhough LDH level and test classification are associated with each other,they are both simultaneous independently significant predictors of OSand TTP. In addition, we have examined the dependence of the Early/Lateclassification on tumor size. Classification was significantlyassociated with tumor size (Mann-Whitney p<0.001), with the median tumorsize in the Early classification group being 53 cm, compared with 16 cmin the Late classification group. However, while tumor size only showeda trend to significance as a predictor of OS and TTP (p=0.065 andp=0.07, respectively) in univariate analysis, Early and Lateclassification retained its highly significant predictive power of OSand TTP when adjusted for tumor size (p<0.001 for OS and p=0.004 forTTP).

Given the magnitude of the difference in outcomes between theclassification groups Early and Late and its independence from otherclinical information, the classifiers developed can provide additionalinformation to physicians and patients to inform the decision whethernivolumab (or another anti-PD-1 antibody), or an alternative therapy isan appropriate treatment for the patient. A final classifier which wouldbe considered preferred would be one generated from all the 119 patientsamples in the development set with OS filtering (approach 1 in Table13, Kaplan-Meier plots of FIGS. 13C and 13D). This classifier isreferred to as the full-set classifier or Example 1 or “IS2” later inthis document.

Independent Validation of the Classifier Generated Using FIG. 8 with aSecond Set of Samples

A set of 30 pretreatment samples from patients treated with anti-PD-1antibodies at Yale University were available as an independentvalidation cohort.

Deep MALDI spectra were generated from these samples and processed usingidentical procedures to those used in classifier development of FIG. 8and described previously. The classifier of “approach 1” for the wholeset (table 13 approach 1) was applied to the resulting feature table,yielding a classification of “Early” or “Late” for each sample. Tensamples were classified as “Early” and the remaining 20 as “Late”. TheKaplan-Meier plot of overall survival for the cohort is shown in FIG. 14and a summary of the analysis of OS is given in Table 16. As was thecase with FIG. 13, note the clear separation between the Early and Lategroups in FIG. 14, indicating the ability of the final classifier ofTable 13 and FIG. 13 to correctly classify the samples in theindependent validation cohort.

TABLE 16 Summary of the performance of the classifier on the Yaleanti-PD-1 antibody-treated cohort OS HR (95% OS log- OS Median#Early/#Late CI) rank p (Early, Late) 10/20 0.27 0.0024 221, 1471 (0.05-(days) 0.52)

The classifier of Example 1 also validated well on an independent cohortof 48 melanoma patients treated with the anti-CTLA4 antibody ipilimumab.See the Example 3 section below.

Protein Identification

Our approach to generating a classifier is based on a correlationalanalysis relating peak intensities to clinical outcome using the CMC/Dprocess described above in conjunction with FIG. 8. As such, theproteins underlying the feature definitions used in the classificationsmay not be causally related to the outcome to treatment. It is also nottrivial to relate peaks measured in a MALDI-TOF experiment to previouslyidentified proteins and their functions. However, by studying theliterature of observed serum proteins in MALDI-TOF studies it is stillpossible to give names to some of the peaks used in our classification.A tentative list is listed in Table 17:

TABLE 17 Tentative assignment of a subset of features used forclassification to proteins/protein fragments Mass [Da] Tentative proteinidentification 4133 c1 inhibitor 4264 ITIH4, C1 fragment 4381Beta-defensin 4A 5867 Leukocyte-specific transcript 1 protein 5889Leukocyte-specific transcript 1 protein 5911 Gamma sectretase C-terminalfragment 50 of Amyloid beta A4 protein 5997 Granulin-3 (Granulin-B) 7318C-C motif chemokine 20 8413 complement C3a 9109 Apolipoprotein C3 9226CDC42 small effector protein 2 10285 B melanoma antigen 3 11686 SerumAmyloid A (SAA) 23469 C-reactive protein (CRP)

While the appearance of acute phase reactants like SAA and CRP wasexpected, we were surprised to find some evidence of proteins related tothe complement system (complement C3a, c1 inhibitor, C1 fragment). Thecomplement system is involved in the immune response and cancerimmunotherapy (Markiewski et al., Is complement good or bad for cancerpatients? A new perspective on an old dilemma. Trends Immunol. 30:286-292 (2009)), and has recently been suggested to have a role in PD-1inhibition (Seng-Ryong Woo, et al., Innate Immune Recognition of CancerAnnual Review of Immunology, Vol. 33: 445-474 (2015).

It appears that at least some part of the classification of patientsinto Early and Late groups is related to an interplay between acutephase reactants and the activation of the complement system. A moredetailed explanation of the relationship between the features forclassification, and the biological functions of the Early and Late classlabels is set forth later in this document in Example 6.

Conclusions on the Classifiers we Developed in Example 1

Applying the DIAGNOSTIC CORTEX™ procedure (FIG. 8) to pre-treatmentserum spectra from melanoma patients obtained using Deep MALDI spectralacquisition we have identified clinical groups “Early” and “Late”. Thesegroups are showing significant differences in outcome (both TTP and OS)following treatment with an anti-PD-1 treatment, nivolumab. We havepresented a test procedure to identify these groups from pre-treatmentsamples, and validated the results in internal validation sets, and inan external validation set.

Patients whose serum classifies as “Early” exhibit significantly fasterprogression and shorter survival than patients whose serum classifies as“Late” making this test suitable as a biomarker for nivolumab therapy.The clinical groups “Early” and “Late” are not associated with PD-L1expression, and our classification remains a significant predictor foroutcome (both TTP and OS) even when other clinical attributes areincluded in a multivariate analysis.

While a correlative approach to test development does not easily lenditself to a deep understanding of the biology underlying the differencebetween the identified groups, we have done some initial work relatingthe two different groups to differences in acute phase reactants and thecomplement system. The success of this project exemplifies the power ofthe combination of Deep MALDI spectral acquisition and our classifierdevelopment methods in the construction of clinically useful tests.

The following clauses are offered as further descriptions of theinventions disclosed in Example 1.

1. A method of predicting melanoma patient benefit to an antibody drugblocking ligand activation of programmed cell death 1 (PD-1), comprising

a) conducting mass spectrometry on a blood-based sample of the patientand obtaining mass spectrometry data;

(b) obtaining integrated intensity values in the mass spectrometry dataof a multitude of mass-spectral features; and

(c) operating on the mass spectral data with a programmed computerimplementing a classifier;

wherein in the operating step the classifier compares the integratedintensity values with feature values of a reference set of class-labeledmass spectral data obtained from blood-based samples obtained from amultitude of other melanoma patients treated with the drug with aclassification algorithm and generates a class label for the sample,wherein the class label “early” or the equivalent predicts the patientis likely to obtain relatively less benefit from the antibody drug andthe class label “late” or the equivalent indicates the patient is likelyto obtain relatively greater benefit from the antibody drug.

2. The method of clause 1, wherein the melanoma patient providing theblood-based sample has been treated previously with ipilimumab.3. The method of clause 2, wherein the patient had high grade toxicityto ipilimumab.4. The method of any of clauses 1-3, wherein the mass spectral featuresinclude a multitude of features listed in Appendix A, Appendix B, orAppendix C.5. The method of any of clauses 1-4, wherein the classifier is generatedfrom a combination of filtered mini-classifiers using a regularizedcombination method.6. The method of any of clauses 1-5, wherein the mass spectral data isacquired from at least 100,000 laser shots performed on the sample usingMALDI-TOF mass spectrometry.7. The method of clause 6, wherein the mini-classifiers are filtered inaccordance with any one of the criteria listed in Table 10.8. The method of clause 5, wherein the classifier is defined from amultitude of master classifiers generated from a multitude ofseparations of a development set of samples into a training set and atest set.9. The method of clause 5, wherein the classifier is developed from asample set including patients with and without prior treatment fromipilimumab.10. The method of clause 5, wherein the regularized combination methodcomprises repeatedly conducting logistic regression with extremedropout.11. The method of any of clauses 1-10 wherein the antibody drugcomprises nivolumab.12. The method of any of clauses 1-11, wherein the relatively greaterbenefit associated with the Late label means significantly greater(longer) overall survival as compared to the Early class label.13. The method of any of clauses 1-12, wherein the reference set isderived from class-labeled blood-based samples from melanoma patientstreated with nivolumab, wherein the class-labeled blood-based samples inthe reference set have class labels of Early or Late, or the equivalent,and wherein the Late samples had greater overall survival on nivolumabas compared to the Early samples.14. A machine (see FIG. 15) predicting melanoma patient benefit from anantibody drug blocking ligand activation of programmed cell death 1(PD-1), comprising:

a memory storing a reference set in the form of feature values for amultitude of mass spectral features obtained from mass spectra ofblood-based samples from a multitude of melanoma patients treated withthe antibody drug;

the memory further storing a set of code defining a final classifierbased on a multitude of master classifiers, each master classifiergenerated from filtered mini-classifiers combined using a regularizedcombination method;

a central processing unit operating on the set of code and the referenceset and mass spectral data obtained from a blood-based sample of amelanoma patient to be tested and responsively generating a class labelfor the blood-based sample, wherein the class label “Early” or theequivalent predicts the patient is likely to obtain relatively lessbenefit from the antibody drug and the class label “Late” or theequivalent indicates the patient is likely to obtain relatively greaterbenefit from the antibody drug.

15. The machine of clause 14, wherein the mass spectral data for thedevelopment set and the sample are acquired from at least 100,000 lasershots performed on the samples forming the development set and thesample to be tested using MALDI-TOF mass spectrometry.16. The machine of clause 14, wherein the mini-classifiers are filteredin accordance with any one of the criteria listed in Table 10.17. The machine of clause 14, wherein the final classifier is definedfrom a multitude of master classifiers each generated from a separationof a classifier development set of samples into a training set and atest set.18. The machine of clause 14, wherein the reference set includes massspectral data from patients with and without prior treatment fromipilimumab.19. The machine of clause 14, wherein the regularized combination methodcomprises logistic regression with extreme dropout.20. The machine of any of clauses 14-19, wherein the drug comprisesnivolumab.21. The machine of any of clauses 14-20, wherein the mass spectralfeatures include a multitude of features listed in Appendix A, AppendixB, or Appendix C.22. The machine of any of clauses 14-21, wherein the relatively greaterbenefit associated with the Late label means significantly greater(longer) overall survival as compared to the Early class label.23. A system for predicting patient benefit from an antibody drugblocking ligand activation of programmed cell death 1 (PD-1),

a mass spectrometer and

the machine of any one of clauses 14-22.

24. A method of generating a classifier for predicting patient benefitfrom an antibody drug blocking ligand activation of programmed celldeath 1 (PD-1), comprising:

1) obtaining mass spectrometry data from a development set ofblood-based samples obtained from melanoma patients treated with theantibody drug, in which a mass spectrum from at least 100,000 lasershots is acquired from each member of the set;

2) performing spectral pre-processing operations on the mass spectraldata from the development sample set, including background estimationand subtraction, alignment, batch correction, and normalization;

3) performing the process of FIG. 8 steps 102-150 including generating amaster classifier based on regularized combination of a filtered set ofmini-classifiers for each separation of the development set of samplesinto training and test sets;

4) evaluating master classifier performance of the classifiers generatedin accordance with step 3); and

5) defining a final classifier based on the master classifiers generatedin step 3).

25. The method of clause 24, wherein the final classifier includes areference set including feature values for a set of features listed inAppendix A, Appendix B, or Appendix C.26. The method of clause 24, wherein integrated intensity values areobtained for each of the features listed in Appendix A, and wherein themethod further comprises the step of deselecting features from the listof features of Appendix A which are not contributing to classifierperformance and performing steps 3), 4), and 5) using a reduced list thefeatures.27. The method of clause 26, wherein the reduced list of featurescomprises the list of features in one of the sets of Appendix B or thelist of features in Appendix C.28. The method of clause 24, wherein the filtering criteria comprise thefiltering criteria of Table 10.29. A method for treating a melanoma patient, comprising

administrating an antibody drug blocking ligand activation of PD-1 tothe patient,

wherein a blood-based sample of the patient has been previously assignedthe class label of Late or the equivalent from performing the method ofany one of clauses 1-13 on the blood-based sample.

30. An improved general purpose computer configured as a classifier forclassifying a blood-based sample from a human cancer patient to make aprediction about the patient's survival or relative likelihood ofobtaining benefit from a drug, comprising:

a memory storing a reference set in the form of feature values for amultitude of mass spectral features obtained from mass spectrometry ofblood-based samples from a multitude of melanoma patients treated withan immune checkpoint inhibitor and an associated class label for each ofthe blood-based samples in the training set, the blood based samplesforming a classifier development set;

the memory further storing a set of computer-executable code defining afinal classifier based on a multitude of master classifiers, each masterclassifier generated from a set of filtered mini-classifiers executing aclassification algorithm and combined using a regularized combinationmethod; wherein the multitude of master classifiers are obtained frommany different realizations of a separation of the development set intoclassifier training and test sets; and

a central processing unit operating on the set of code, the referenceset, and mass spectral data obtained from the blood-based sample of thecancer patient to be tested and generating a class label for theblood-based sample.

31. The improved computer of clause 30, wherein the memory storesfeature values for at least 50 of the features listed in Appendix A.

Example 2 Predictive Classifier for Melanoma Patient Benefit from ImmuneCheckpoint Inhibitors

The classifier of Example 1, including the classifiers built from onehalf of the sample set and the classifier built using the whole set ofsamples, showed similar performance and validated well on twoindependent sample sets. It was discovered that the classifiers ofExample 1 also split a cohort of 173 first line, advanced non-small celllung cancer (NSCLC) patients treated with platinum-doublet+cetuximab,and yet another cohort of 138 ovarian cancer patients treated withplatinum-doublet after surgery into groups with better and worse OS andprogression-free survival (PFS). For further details on this discovery,see Example 4 below.

It is possible to argue that the classifiers of Example 1 above have astrong prognostic component, since they seem to stratify patientsaccording to their outcomes regardless of whether treatment wasimmunotherapy or chemotherapy. While clinically this might or might notmatter, Example 2 describes the development of a classifier and testcapable of splitting patients treated with immune checkpoint inhibitorsaccording to their outcome, while not stratifying outcomes of patientstreated with chemotherapy—i.e., a predictive test between immunecheckpoint inhibitors and chemotherapy with less prognostic component.

Patient Samples

The samples we used to develop the classifier of Example 2 werepretreatment serum samples from two different cohorts. The first cohortwas the 119 samples from melanoma patients treated with nivolumab, anddiscussed at length in Example 1, see Table 1 above for the baselineclinical characteristics, FIGS. 1A and 1B, etc. The set was split intodevelopment and validation sets as explained above in Example 1.

A second cohort, referred to as the ACORN NSCLC cohort, was a set of 173pre-treatment serum samples, clinical data and associated mass spectrafrom non-small cell lung cancer (NSCLC) patients treated withplatinum-doublet plus cetuximab. The purpose of the second cohort was totune the development of a classifier such that it was (a) predictive forpatient benefit on nivolumab but also (b) not outcome predictive inNSCLC patients treated with chemotherapy. Available outcome dataincluded progression-free-survival (PFS) and overall survival (OS).Selected clinical characteristics for patients with available spectrafrom pretreatment samples are listed in table 18.

TABLE 18 Selected baseline characteristics of NSCLC patients withavailable spectra N (%) Gender Male 108 (62) Female  65 (38) Age Median(Range) 66.4 (35.4-86.3) PFS Median (months) 4.3 OS Median (months) 9.5Kaplan-Meier plots for progression-free-survival (PFS) and overallsurvival (OS) for the second cohort of 173 NSCLC patients with baselinesamples and acquired spectra are shown in FIGS. 16A and 16B.

The ACORN NSCLC cohort was split into 3 subsets: 58 samples (referred toas “AddFilt” subset herein) were assigned for additional filtering ofmini-classifiers as explained in detail below; 58 samples (the “Test”subset herein) were used for testing the classifier performance togetherwith the melanoma development set samples; and 57 samples (the“Validation” subset herein) were used as part of the internal validationset, in addition to the melanoma validation subset already held for thatpurpose. A method was implemented in order to choose this split whileensuring the 3 subsets were balanced. The details of how we generatedthese splits are not particularly important. Suffice it to say that weanalyzed mass spectral features which were correlated with overallsurvival, and chose the subsets such that it minimized the difference insuch features across the three subsets. The clinical characteristics arelisted for each of the subsets in table 19 and comparison of thetime-to-event data is shown in FIGS. 17A and 17B.

TABLE 19 Baseline characteristics of NSCLC patients with availablespectra for each of the 3 created subsets “Validation” “AddFilt” subset“Test” subset subset (N = 58) n (%) (N = 58) n (%) (N = 57) n (%) GenderMale 19 (33) 22 (38) 24 (42) Female 39 (67) 36 (62) 33 (58) Age Median68.3 66.8 62.0 (Range) (35.4-86.3) (42.3-85.0) (44.9-76.7) PFS Median5.0 4.1 4.4 (months) OS Median 12.4 7.5 9.1 (months)

Sample Preparation

The sample preparation for the serum samples of both the ACORN NSCLCcohort was the same as the nivolumab cohort and described in detail inExample 1.

Spectral Acquisition

The acquisition of mass spectral data for the ACORN NSCLC cohort was thesame as the nivolumab cohort and described in detail in Example 1.

Spectral Processing

The mass spectral data processing, including raster spectrapreprocessing, Deep MALDI average spectra preprocessing (backgroundsubtraction, normalization, alignment) and batch correction, was thesame as described in Example 1. In the project of Example 2 we did notuse feature 9109 in the development of the classifier; however, we usedall other 350 features listed in Appendix A.

Classifier Development

We used the classifier development process of FIG. 8 (described atlength in Example 1) in developing the classifier of Example 2.

Definition of Class Labels (Step 102, FIG. 8A)

The classifier development of Example 2 makes use of OS data from themelanoma/nivolumab development set for initial assignment ofclassification group labels to the samples in the development set. Inthis situation, class labels are not obvious and, as shown in FIG. 8,the classifier development process uses an iterative method to refineclass labels at the same time as creating the classifier. See FIG. 8B,step 144 and loop 146. An initial guess is made for the class labels inthe initial iteration of the method, at step 102. The samples weresorted on OS and half of the samples with the lowest time-to-eventoutcome were assigned the “Early” class label (early death, i.e. pooroutcome) while the other half were assigned the “Late” class label (latedeath, i.e. good outcome). A classifier was then constructed inaccordance with FIG. 8 using the outcome data and these class labels.This classifier was used to generate classifications for all of thedevelopment set samples. The labels of samples that persistentlymisclassified across the ensemble of master classifiers created for themany splits into training and test sets (loop 146) were flipped. Thisrefined set of class labels were then used as the new class labels for asecond iteration of the method at step 102. This process was iterateduntil convergence, in exactly the same way as for Example 1.

Select Training and Test Sets (108, FIG. 8A)

The development set samples were split into training and test sets (step108) in multiple different random realizations. Six hundred and twentyfive realizations were used (i.e. iterations through loop 135). Themethodology of FIG. 8 works best when the training classes Early andLate have the same number of samples in each realization or iterationthrough loop 135. Hence, if the training classes had different numbersof members, they were split in different ratios into test and training.

Creation and Filtering of Mini-Classifiers (120, 126, FIG. 8A)

Many k-nearest neighbor (kNN) mini-classifiers (mCs) that use thetraining set as their reference set were constructed in step 120 usingsubsets of features. In this project we used k=9. To be able to considersubsets of single and two features and improve classifier performance,we deselected features that were not useful for classification from theset of 350. This was done using a bagged feature selection approach asoutlined in Appendix F of our prior provisional application Ser. No.62/289,587 and in U.S. patent application of J. Roder et al. Ser. No.15/091,417.

To target a final classifier that has certain performancecharacteristics, these mCs were filtered in step 126 of FIG. 8A asfollows. Each mC is applied to its training set and to the “AddFilt”NSCLC subset, and performance metrics are calculated from the resultingclassification. Only mCs that satisfy thresholds on these performancemetrics pass filtering to be used further in the process. The mCs thatfail filtering are discarded. For this project hazard ratio based on OSfiltering was used. Table 20 shows the filtering criteria at step 126used for deselecting mini-classifiers in each label-flip iteration (step146) of the classifier development. In every iteration a compound filterwas constructed by combining two single filters with an “AND” operation,see Table 21. Such filtering criteria were designed so that patientstreated with immunotherapy would split according to their OS outcomesbut patients treated with chemotherapy would not.

TABLE 20 Criteria used in the filtering step HR range HR range (melanomasamples, (NSCLC samples, Iteration training subset) “AddFilt” subset) 02.0-10.0  0.9-1.111 1 2.0-10.0 0.8-1.25 2 2.0-10.0 0.8-1.25 3 2.0-10.00.8-1.25 4 2.0-10.0 0.8-1.25

TABLE 21 Parameters used in mini-classifiers and filtering Depth(parameter s, max # features per mC) Filter 2 HR on OS (melanomasamples) .and. HR on OS (NSCLC samples), see Table 20 for HR values

Combination of mini-classifiers using logistic regression with dropout(steps 130, 132)

Once the filtering of the mCs was complete, the mCs were combined in onemaster classifier (MC) using a logistic regression trained using thetraining set class labels. To help avoid overfitting the regression isregularized using extreme drop out with only a small number of the mCschosen randomly for inclusion in each of the logistic regressioniterations. The number of dropout iterations was selected based on thetypical number of mCs passing filtering to ensure that each mC waslikely to be included within the drop out process multiple times. In alllabel-flip iterations, ten randomly selected mCs were taken in each ofthe 10,000 drop out iterations.

Although the ACORN NSCLC “AddFilt” subset of samples has been used inthe filtering step as described above, such samples were not included inthe training of the logistic regression that combines the mCs (or thereference sets of the kNN mini-classifiers). Only the training subsetdrawn from the development set (melanoma samples) was used for thatpurpose.

Training/Test Splits (Loop 135 of FIG. 8A)

The use of multiple training/test splits in loop 135, and in thisExample 625 different training/test set realizations, avoids selectionof a single, particularly advantageous or difficult, training set forclassifier creation and avoids bias in performance assessment fromtesting on a test set that could be especially easy or difficult toclassify.

Definition of Final Test in Step 150

The output of the logistic regression that defines each MC is aprobability of being in one of the two training classes (Early or Late).These MC probabilities over all the 625 training and test set splits canbe averaged to yield one average probability for a sample. When workingwith the development set, this approach is adjusted to average over MCsfor which a given sample is not included in the training set(“out-of-bag” estimate). These average probabilities can be convertedinto a binary classification by applying a threshold (cutoff). Duringthe iterative classifier construction and label refinement process,classifications were assigned by majority vote of the individual MClabels obtained with a cutoff of 0.5. This process was modified toincorporate only MCs where the sample was not in the training set forsamples in the development set (modified, or “out-of-bag” majorityvote). This procedure gives very similar classifications to using acutoff of 0.5 on the average probabilities across MCs.

Results

The performance of the classifiers developed in Example 2 was assessedusing Kaplan-Meier plots of OS between samples classified as Early andLate, together with corresponding hazard ratios (HRs) and log-rank pvalues. This performance estimation was performed separately formelanoma patients treated with nivolumab and for NSCLC samples treatedwith chemotherapy. The results are summarized in table 22 for the timeendpoints available for each sample set. Kaplan-Meier plotscorresponding to the data in table 22 are shown in FIGS. 18A-18H. Theplots of FIG. 18A-H and the corresponding results of table 22, in thecase of the ACORN NSCLC set, did not consider any of the “AddFilt”subset samples since they were directly used in the mC filtering step inall of the 625 training/test realizations. The classifications of all119 samples in the melanoma cohort are listed in Appendix I of our priorprovisional application Ser. No. 62/289,587, together with theirclassifications obtained from Example 1 for comparison. The featuresused in the final label flip iteration of this classifier developmentare given in Appendix C.

TABLE 22 Performance summary OS HR OS OS Median TTP HR TTP TTP #Early/(95% log- (Early, (95% log- Median #Late Cl) rank p Late) Cl) rank p(Early, Late) Development 25/35 0.42 0.014 61, 145 0.53 0.034 84, 285(melanoma (0.18-0.81) (weeks) (0.27-0.94) (days) nivo) Validation 18/410.52 0.058 55, 99 0.43 0.004 80, 183 (melanoma (0.21-1.02) (weeks)(0.17-0.70) (days) nivo) PFS HR PFS PFS (95% log- Median Cl) rank p(Early, Late) Development 35/23 0.60 0.089 7.0, 10.1 0.81 0.434 3.9, 4.2(NSCLC (0.34-1.07) (months) (0.48-1.37) (months) chemo) Validation 32/250.99 0.962 9.0, 9.5 0.98 0.952 4.2, 5.1 (NSCLC (0.54-1.81) (months)(0.58-1.68) (months) chemo)Note that in FIGS. 18A-18D, for the melanoma/nivolumab cohort there is aclear separation in the OS and TTP curves between the classes Early andLate in both the development and validation sets, whereas in theNSCLC/chemotherapy cohort there is a little or no separation between theEarly and Late classes in the development and validation sets. Thisdemonstrates that our use of the NSCLC cohort for filteringmini-classifiers in the development of the Master Classifiers and in thedefinition of the final classifier was useful in creating a classifierthat is predictive of benefit of nivolumab in melanoma but does notstratify outcomes of NSCLC patients treated with chemotherapy.

Baseline clinical characteristics of the melanoma/nivolumab sample set(development+validation) are summarized by classification group in table23. Clinical characteristics of the ACORN NSCLC sample set(“Test”+“Validation”) are also summarized by classification group intable 24.

TABLE 23 Clinical characteristic by classification group (melanomasample set) Early (N = 43) Late (N = 76) n (%) n (%) Gender Male 28 (65)44 (58) Female 14 (33) 31 (41) NA 1 (2) 1 (1) Age Median   63 (23-87) 60.5 (16-79) (Range) Response PR  7 (16) 24 (32) SD 3 (7) 15 (20) PD 33(77) 37 (49) Cohort 1 1 (2)  8 (11) 2 4 (9) 7 (9) 3 3 (7)  8 (11) 4  5(12) 5 (7) 5  5 (12) 16 (21) 6 25 (58) 32 (42) Prior Ipi No  8 (19) 23(30) Yes 35 (81) 53 (70) VS-like good 24 (56) 74 (97) classificationpoor 19 (44) 2 (3) PD-L1 expression Positive 3 (7) 5 (7) (5% tumor)Negative 11 (26) 18 (24) NA 29 (67) 53 (70) PD-L1 expression Positive  7(16) 11 (14) (1% tumor) Negative  7 (16) 12 (16) NA 29 (67) 53 (70)PD-L1 expression Positive 10 (23) 18 (24) (1% tumor/ Negative 3 (7) 4(5) immune cells) NA 30 (70) 54 (71) LDH level ^(x) Median 655(414-1292) 469 (353-583) (Range) (IU/L) >ULN ^(x x) 40 (93) 60(80) >2ULN 21 (49) 10 (13) ^(x) Missing for one patient, ^(x x) ULN =upper limit of normal range

TABLE 24 Clinical characteristic by classification group (ACORN NSCLCsample set) Early Late (N = 67) n (N = 48) n (%) (%) Gender Male 37 (55)32 (67) Female 30 (45) 16 (33) Age Median (Range) 66.5 (42.3-85.0) 63.4(46.3-80.7)

The reader will note that the data in Table 14 differs slightly from thedata in Table 23. In particular, the classifications we obtain from theclassifier of Example 2 are not exactly the same as the classificationswe get from the Example 1 classifier; although not surprisingly asizeable proportion of samples get the same label for both classifiers.See Appendix I of our prior provisional application Ser. No. 62/289,587for the labels produced from the Example 1 and Example 2 classifiers forall 119 samples. The reader will also note that Appendix C lists thesubset of the 350 features that we used for the final classifier ofExample 2.

The idea behind the reduced features set of Appendix C for theclassifier of Example 2 is that instead of just taking all of the mCs(and associated features) that give a prognostic behavior for treatmentwith nivolumab, in the Example 2 classifier we only use the subset ofmCs that, in addition to being useful to predict benefit/nonbenefit fromnivolumab treatment, show no separation on the ACORN NSCLC cohort. So,we use a smaller subset of all mCs as we have an additional constrainton their behavior. Then, when we combine these mC, we obtain a differentclassifier with different sample classifications and different behavior(at least on the ACORN NSCLC set). To get the different behavior on theACORN set, we have to get some different labels on the melanoma set—butnot enough to destroy the nice separation between the Early and Latepopulations that we had with the Example 1 classifier. A priori it isnot clear that it is possible to do this, but the results of Example 2demonstrate that it is possible to generate such a classifier.

The results of a multivariate analysis of the melanoma sample set(development+validation) are shown in table 25. Two samples, for whichgender was not available, and one sample without LDH level were notconsidered in such analysis.

TABLE 25 Multivariate analysis of OS and TTP for the melanoma set(Development + Validation) OS TTP Covariate HR (95% CI) P value HR (95%CI) P value Late vs Early 2.38 0.002 2.22 <0.001 (1.37-4.17) (1.41-3.57)Male vs Female 1.73 0.052 1.76 0.017 (1.00-3.01) (1.11-2.79) Prior Ipi(no vs yes) 0.63 0.122 0.66 0.115 (0.35-1.13) (0.40-1.11) PD-L1 (5%)0.69 0.506 1.10 0.825 −ve vs +ve (0.23-2.06) (0.46-2.64) PD-L1 (5%) 0.810.491 1.04 0.848 −ve vs NA (0.44-1.48) (0.61-1.79) LDH (IU/L)/1000 1.740.001 1.54 0.009 (1.25-2.44) (1.12-2.11)

Even though the classification of Example 2 is significantly associatedwith LDH level (table 23), both quantities are independently significantpredictors of TTP and OS in multivariate analysis. In addition, analysisof tumor size showed that although strongly associated with Early andLate classification (Mann-Whitney p<0.001), with the median tumor sizebeing 48 cm in the Early group and 16 cm in the Late group, Early/Lateclassification retained its significance as a predictor of OS and TTPwhen adjusted for tumor size (p=0.014 for OS and p=0.005 for TTP). Thusthe classification has independent predictive power in addition to otherprognostic factors such as LDH and tumor size.

Independent Validation of Example 2 Classifier

The developed classifier was applied to several sample sets from twodifferent cancer types (melanoma and ovarian) and therapies (immunecheckpoint inhibitors and chemotherapies). For all samples Deep MALDIspectra were generated and processed using identical procedures to thoseused in development. For each sample a classification of “Early” or“Late” was obtained. For each cohort, Kaplan-Meier plots of theavailable time endpoints are shown and a summary of the analysis oftime-to-event is given.

A. Yale Anti-PD-1 Cohort

The following results refer to a set of 30 pretreatment samples frompatients with advanced unresectable melanoma treated with anti-PD-1antibodies at Yale University. FIG. 19 is a Kaplan-Meier plot of overallsurvival for the Yale cohort of patients. It shows a clear separation ofthe survival curves between the two classes Early and Late. Thestatistics for the results are shown in Table 26.

TABLE 26 Summary of the performance of the classifier on the Yaleanti-PD-1 antibody-treated cohort OS HR OS OS Median # Early/# Late (95%CI) log-rank p (Early, Late) 7/23 0.37 0.034 221, 832 (0.07-0.89) (days)These results show that the classifier of Example 2 generalized well toan independent sample set.The baseline clinical data available for this cohort of patients issummarized in table 27.

TABLE 27 Baseline characteristics of the cohort n (%) Gender Male 19(63) Female 11 (37) Age Median (Range)  55.5 (26-83) Race White 29 (97)Black 1 (3) VeriStrat Label Good 24 (80) Poor  6 (20)

We obtained qualitatively similar results for this cohort of anti-PD-1antibody treated patients for the full-set classifier developed inaccordance with Example 1, Table 13, OS filtering.

B. Yale Anti-CTLA4 Cohort

The following results refer to a set of 48 pretreatment samples fromadvanced, unresectable melanoma patients treated with anti-CTLA-4antibodies (ipilimumab or other similar antibodies) at Yale University.The small amount of baseline clinical data available for this cohort ofpatients is summarized in table 28.

TABLE 28 Baseline characteristics of the Yale anti-CTLA4 cohort n (%)Gender Male 31 (65) Female 17 (35) Race White  48 (100) VeriStrat LabelGood 40 (83) Poor  8 (17)

FIG. 20 is a Kaplan-Meier plot of overall survival for the Yaleanti-CTLA4 cohort by classification produced by the classifier ofExample 2. Note the clear separation of the Early and Late classesproduced by the classifier of Example 2 on this cohort, indicating theclassifier's ability to predict melanoma patient benefit fromadministration of anti-CTLA4 antibodies. The statistics for theperformance of the classifier on this cohort are set forth in table 29.

TABLE 29 Summary of the performance of the classifier on the Yale cohorttreated with ipilimumab OS HR OS OS Median # Early/# Late (95% CI)log-rank p (Early, Late) 16/32 0.27 <0.0001 156, 782 (0.05-0.28) (days)

B. Ovarian Chemotherapy

The following results refer to a set of 138 pretreatment samples frompatients with ovarian cancer treated with platinum-doublet chemotherapyafter surgery. Two of these patients have no disease-free survival (DFS)data. The Kaplan-Meier plots for the ovarian cancer cohort byclassification produced by the classifier of Example 2 are shown in FIG.21A (overall survival) and FIG. 21B (disease free survival). Note thatthe classifiers did not produce a stratification of the Early and Lateclassification groups in this cohort. This is consistent with theclassifier not stratifying the NSCLC patients treated with chemotherapy.Statistics for the classifier performance shown in FIGS. 21A and 21B areset forth in Table 30.

TABLE 30 Summary of the performance of the classifier on the cohort ofpatients with ovarian cancer treated with platinum-doublet chemotherapyafter surgery OS HR OS log- OS Median DFS HR DFS log- DFS Median#Early/#Late (95% CI) rank p (Early, Late) (95% CI) rank p (Early, Late)77/61 0.98 0.922 41, 41 (months) 0.94 0.787 25, 27 (months) (0.61-1.56)(0.60-1.47)

Reproducibility

Two sample sets (the melanoma/nivolumab and Yale anti-CTLA4 cohorts)were rerun to assess the reproducibility of the classifier of Example 2.Spectra were acquired in completely separate batches from the originalruns. In all cases, the mass spectrometer used for the original massspectral acquisition and the rerun mass spectral acquisition had beenused on other projects of the assignee in the interim. Reproducibilityof the classifier labels is summarized in table 31.

A. The Melanoma/Nivolumab Cohort

The classifier was concordant between the original run and rerun in 113of the 119 samples, for an overall concordance of 95%. Of the 43 samplesoriginally classified as Early, 38 were classified as Early and 5 asLate in the rerun. Of the 76 samples originally classified as Late, 75were classified as Late and 1 classified as Early in the rerun.

B. Yale Anti-CTLA4 Cohort

The reproducibility of the classifier was evaluated only in a subset of43 samples of the cohort. The classifier was concordant between theoriginal run and rerun in 40 of those 43 samples, for an overallconcordance of 93%. Within the subset of 43 samples, of the 13originally classified as Early, 12 were classified as Early and 1 asLate in the rerun. Of the 30 samples originally classified as Late, 28were classified as Late and 2 classified as Early in the rerun.

TABLE 31 Reproducibility of the classifications assigned by thepredictive classifier across sample sets Sample Set Label concordancenivolumab 113/119 (95%) anti-CTLA4  40/43 (93%)

Example 2 Conclusions

We were able to construct a classifier that could separate patientstreated with immune checkpoint inhibitors into groups with better andworse outcomes (TTP, OS), while not separating patients treated withchemotherapies according to their outcome (OS and PFS or DFS). Theclassifier was constructed using deep MALDI mass spectra generated frompretreatment serum samples, and was trained using half of the available119 melanoma samples treated with nivolumab. One third of the 173available NSCLC samples treated with platinum-doublet plus cetuximabchemotherapy was used to tune the classifier to be predictive and notjust prognostic.

The classifier generalized well by stratifying by outcome twoindependent cohorts of melanoma patients treated with immune checkpointinhibitors and not stratifying by outcome a third cohort of ovariancancer patients treated with post-surgery platinum-doublet chemotherapy.

The classifier demonstrated acceptable reproducibility on two separatecohorts, with concordance of 93% or higher.

The following clauses are offered as further descriptions of theinventions disclosed in Example 2:

1. An improved general purpose computer configured as a classifier forclassifying a blood-based sample from a human cancer patient to make aprediction about the patient's survival or relative likelihood ofobtaining benefit from a drug, comprising:

a memory storing a reference set in the form of feature values for amultitude of mass spectral features obtained from mass spectrometry ofblood-based samples from a multitude of melanoma patients treated withan immune checkpoint inhibitor and an associated class label for each ofthe blood-based samples in the training set, the blood based samplesforming a classifier development set;

the memory further storing a set of computer-executable code defining afinal classifier based on a multitude of master classifiers, each masterclassifier generated from a set of filtered mini-classifiers executing aclassification algorithm and combined using a regularized combinationmethod; wherein the multitude of master classifiers are obtained frommany different realizations of a separation of the development set intoclassifier training and test sets; and

a central processing unit operating on the set of code, the referenceset, and mass spectral data obtained from the blood-based sample of thecancer patient to be tested and generating a class label for theblood-based sample; and

wherein the mini-classifiers are filtered, in part, by classifierperformance of the mini-classifiers on feature values for a set of massspectral data obtained from blood-based samples of non-small cell lungcancer (NSCLC) patients.

2. The improved computer of clause 1, wherein the set of code isprogrammed to generate a class label for the sample of the form of Earlyor the equivalent or Late of the equivalent, wherein the class labelEarly or the equivalent predicts the patient is likely to obtainrelatively less benefit from an immune checkpoint inhibitor drug and theclass label Late or the equivalent indicates the patient is likely toobtain relatively greater benefit from the immune checkpoint inhibitor.

3. The improved computer of clause 2, wherein the immune checkpointinhibitor comprises a monoclonal antibody blocking ligand activation ofprogrammed cell death 1(PD-1).

4. The improved computer of clause 2, wherein the cancer patientproviding the blood-based sample to be tested has been diagnosed withlung cancer, ovarian cancer, or melanoma.

5. The improved computer of clause 2, wherein the relatively greaterbenefit associated with the Late label means significantly greater(longer) overall survival as compared to the Early class label.

Example 3 Classifier for Predicting Melanoma Patient Benefit fromAnti-CTLA4 Antibodies

FIG. 20 and Table 29 above demonstrate that the classifier of Example 2was able to predict melanoma patient benefit from administration ofanti-CTLA4 antibodies. Ipilimumab is a specific example of such a drug.It is known that the majority of the patients in the Yale cohortreceived ipilimumab. We are not certain that they all receivedipilimumab and not some other anti-CTLA4 antibody under development.These results demonstrate that it is possible to predict from ablood-based sample in advance of treatment whether a melanoma patient islikely to benefit from anti-CTLA4 antibody drugs. Note further that thisclassifier for predicting patient benefit for anti-CTLA4 antibody drugswas developed from a sample set of patients who were treated withnivolumab, which is an anti-PD-1 antibody.

We further found that the full-set classifier of Example 1 alsovalidated well on this cohort. (The term “full-set classifier of Example1” means the classifier developed from all 119 patient samples in thenivolumab cohort in accordance with FIG. 8 as explained above in Example1, see discussion of Table 13, Approach 2, with OS mini-classifierfiltering.) FIG. 23 shows the Kaplan-Meier curves for the Early and Lategroups from the full set classifier of Example 1 applied to this cohort.Table 32 is a summary of the performance of the classifier on the Yaleanti-CTLA4 antibody-treated cohort for the full-set classifier ofExample 1.

TABLE 32 Summary of the performance of the classifier OS HR OS OS Median# Early/# Late (95% CI) log-rank p (Early, Late) 20/28 0.33 0.0002 156,804 (days) (0.10-0.47) 22, 115 (weeks)

TABLE 33 Baseline characteristics of the cohort by classification groupEarly (N = 20) Late (N = 28) n (%) n (%) Gender Male 12 (60) 19 (68)Female  8 (40)  9 (32) VeriStrat Label Good 12 (60)  28 (100) Poor  8(40) 0 (0)A subset of 43 samples from this cohort (16 classified as Early and 27classified as Late) was rerun with independent sample preparation andspectral acquisition. Of the 43 samples, 41 were assigned the same classlabel as in the original run (95% concordance). Two samples initiallyclassified as Early were classified as Late on the rerun.

We also developed a classifier for identification of patients withbetter and worse outcomes on anti-CTLA4 therapy using pre-treatmentserum samples from patients subsequently treated with anti-CTLA4 agents.We will now describe the pertinent aspects of this classifierdevelopment exercise and the performance of the classifier developedfrom this sample set.

In this classifier development exercise, we had 48 pretreatment serumsamples available from a cohort of patients, subsequently treated withanti-CTLA4 agents along with associated clinical data. Most patients areknown to have received ipilimumab, but some may have received analternative anti-CTLA4 antibody. These are the same 48 patients that weran Example 1 and Example 2 validation tests on which are alreadydescribed above in Example 3. Overall survival (OS) was the only outcomeendpoint available.

For this classifier development, we used the same spectra and the samespectral processing and features, i.e. identical feature table, as wedid for the Examples 1 and 2. The only difference was that feature 9109was dropped from the feature table, as we are concerned that it hasreproducibility issues and little value for classification.

We used the same Diagnostic Cortex method of FIG. 8 in classifierdevelopment, as detailed throughout the description of Examples 1 and 2,with label flip iterations. The initial class definitions were based onshorter or longer OS. We used mini-classifier filtering based on hazardratio for OS between the classification groups of the training set.

The resulting classifier assigned 16 patients to the Early group and 32to the Late group. (This compares with 20 in the Early group and 28 inthe Late group from the Example 1 full-set classifier.) The Kaplan-Meierplot of classifier performance is shown in FIG. 23A for the groupsdefined by the new classifier trained on the anti-CTLA4 cohort. Note theclear separation in the overall survival between the Early and Lategroups in the plot of FIG. 23A. None of the patients in the Early groupsurvived more than 3 years. The statistics for the classifierperformance are as follows:

# Early/# Late HR (95% CI) log rank p Median OS 16/32 0.24 (0.04-0.23)<0.0001 Early: 155 days, Late: 804 daysA comparison of this result with the classifier performance we obtainedfor the Example 1 full-set classifier operating on the Yale cohort isshown in FIG. 23B. Note in FIG. 23B the almost perfect overlap betweenthe Early and Late groups in the classifiers developed from themelanoma/nivolumab sample set (“example 1 full-set classifier” and theclassifiers developed from the Yale anti-CTLA4 cohort (“Early” and“Late”).

So, comparing the Kaplan-Meier plots of FIG. 23 and the statistics ofthe two classifiers, the classifier performance results do not seem tobe significantly better with the classifier developed on the anti-CTLA4cohort than with the classifier developed on the nivolumab-treatedcohort. The fact that the two classifiers produce quite similar resultsusing completely different development sets supports our assertions thatthe Diagnostic Cortex method of FIG. 8 produces tests that do notoverfit to development sample sets, but rather extract the informationthat will generalize to other sample sets.

In one embodiment, testing method of making a prediction of whether amelanoma patient is likely to benefit from anti-CTLA4 antibody druginvolves the following steps:

(a) conducting mass spectrometry on a blood-based sample of the patientand obtaining mass spectrometry data;

(b) obtaining integrated intensity values in the mass spectrometry dataof a multitude of pre-determined mass-spectral features (such as forexample the features listed in Appendix A or some subset thereof, e.g.,after a deselection of noisy features that do not significantlycontribute to classifier performance such as the features of one of thesets of Appendix B or Appendix C); and

(c) operating on the mass spectral data with a programmed computerimplementing a classifier (e.g., a classifier generated in accordancewith FIG. 8 as explained in Example 2 or the full-set classifier ofExample 1).

In the operating step the classifier compares the integrated intensityvalues with feature values of a training set of class-labeled massspectral data obtained from blood-based samples obtained from amultitude of other melanoma patients. This training set could be of oneof two types, namely class labeled mass-spectral data from a set ofsamples from patients treated with an antibody drug blocking ligandactivation of programmed cell death 1 (PD-1), e.g., nivolumab.Alternatively, this training set could be class-labeled mass spectraldata from a set of samples from patients treated with an anti-CTLA4antibody. The classifier performs this comparison with a classificationalgorithm. The classifier generates a class label for the sample,wherein the class label “early” or the equivalent predicts the patientis likely to obtain relatively less benefit from the anti-CTLA4 antibodydrug and the class label “late” or the equivalent indicates the patientis likely to obtain relatively greater benefit from the anti-CTLA4antibody drug.

Additionally, preferably the mass spectral data is acquired from atleast 100,000 laser shots performed on the blood-based sample usingMALDI-TOF mass spectrometry.

The classifier is preferably obtained from a combination of filteredmini-classifiers using a regularized combination method. Themini-classifiers may be filtered as explained in Example 2 by retainingmini-classifiers defined during classifier generation so that massspectra from patients treated with immunotherapy would split accordingto their OS outcomes, but when such mini-classifiers are applied to massspectra from patients treated with chemotherapy they would not split.This is considered optional, as the “full-set” classifier of Example 1,which can be used in this test, was developed without using anyfiltering of mini-classifiers on a set of samples from a chemotherapycohort.

A testing environment for conducting the test of Example 3 on ablood-based sample of a melanoma patient can take the form of the systemshown in FIG. 15 and described in detail below in Example 5.

The following clauses are offered as further descriptions of thedisclosed inventions of Example 3.

1. A method of predicting melanoma patient response to an antibody drugtargeting CTLA-4, comprising:

a) conducting mass spectrometry on a blood-based sample of the patientand obtaining mass spectrometry data;

(b) obtaining integrated intensity values of a multitude ofpre-determined mass-spectral features in the mass spectrometry data; and

(c) operating on the mass spectral data with a programmed computerimplementing a classifier;

wherein in the operating step the classifier compares the integratedintensity values with feature values of a reference set of class-labeledmass spectral data obtained from blood-based samples obtained from amultitude of other melanoma patients treated with either (1) an antibodydrug targeting programmed cell death 1 (PD-1) or (2) an antibody drugtargeting CTLA4 with a classification algorithm and generates a classlabel for the sample, wherein the class label “early” or the equivalentpredicts the patient is likely to obtain relatively less benefit fromthe antibody drug targeting CTLA-4, and the class label “late” or theequivalent indicates the patient is likely to obtain relatively greaterbenefit from the antibody drug targeting CTLA4.

2. The method of clause 1, wherein the pre-determined mass spectralfeatures include a multitude of features listed in Appendix A, AppendixB, or Appendix C.

3. The method of clause 1, wherein the classifier is configured as acombination of filtered mini-classifiers using a regularized combinationmethod.

4. The method of clause 1, wherein the mass spectral data is acquiredfrom at least 100,000 laser shots performed on the sample usingMALDI-TOF mass spectrometry.

5. The method of clause 1, wherein the mini-classifiers are filtered inaccordance with any one of the criteria listed in Table 10.

6. The method of clause 1, wherein the classifier is defined from amultitude of master classifiers generated from a multitude ofseparations of a development set of samples into a training set and atest set.

7. The method of clause 1, wherein the reference set is in the form ofclass-labeled mass spectral data obtained from blood-based samplesobtained from a multitude of melanoma patients treated with an antibodydrug targeting programmed cell death 1 (PD-1).

8. A machine predicting melanoma patient benefit of an antibody drugtargeting CTLA-4, comprising:

a memory storing a reference set in the form of feature values for amultitude of mass spectral features obtained from mass spectra ofblood-based samples from a multitude of melanoma patients either (1)treated with an antibody drug blocking ligand activation of programmedcell death 1 (PD-1) or (2) treated with an antibody drug targetingCTLA4;

the memory further storing a set of code defining a final classifierbased on a multitude of master classifiers, each master classifiergenerated from filtered mini-classifiers combined using a regularizedcombination method;

a central processing unit operating on the set of code and the referenceset and mass spectral data obtained from a blood-based sample of amelanoma patient to be tested and responsively generating a class labelfor the blood-based sample, wherein the class label “early” or theequivalent predicts the patient is likely to obtain relatively lessbenefit from the antibody drug targeting CTLA4 and the class label“late” or the equivalent indicates the patient is likely to obtainrelatively greater benefit from the antibody drug targeting CTLA4.

9. The machine of clause 8, wherein the memory stores integratedintensity values for a multitude of features listed in Appendix A, orAppendix B, or Appendix C.

10. A method of treating a melanoma patient, comprising the step ofadministrating an antibody drug targeting CTLA-4 to the patient,

wherein the patient has been previously selected for such administrationby the performance of the method of any of clauses 1-7 on a blood-basedsample of the patient and the patient was assigned the class label ofLate or the equivalent.

Example 4 Classifier for Predicting Better or Worse Survival in Ovarianand NSCLC Patients Treated with Chemotherapy

We discovered that the classifier of Example 1 split a cohort of 173first line, advanced non-small cell lung cancer (NSCLC) patients treatedwith platinum-doublet+cetuximab, and yet another cohort of 138 ovariancancer patients treated with platinum-doublet after surgery into groupswith better and worse OS and progression-free (or disease-free) survival(PFS or DFS). Practical tests for predicting better or worse survival inovarian and NSCLC patients will be described in this section. Furtherdetails on the ACORN NSCLC and ovarian cancer cohorts will also bedescribed in this Example 4. Note that the classifier of Example 2 (withthe use of the ACORN NSCLC cohort for mini-classifier filtering) doesnot identify those ovarian and NSCLC patients which are likely tobenefit from chemotherapy, hence the classifier and test for predictingbetter or worse survival in ovarian and NSCLC on platinum chemotherapyis constructed in accordance with Example 1.

ACORN NSCLC Cohort

A set of 173 pretreatment blood-based samples from patients withpreviously untreated advanced non-small cell lung cancer (NSCLC) wereavailable. Patients received platinum-based chemotherapy with cetuximabas part of a clinical trial. The most important baseline clinical dataavailable for this cohort of patients are summarized in table 34 and OSand progression-free survival (PFS) for the whole cohort is shown inFIGS. 24A and 24B, respectively.

TABLE 34 Baseline characteristics of the ACORN NSCLC cohort n (%) GenderMale 108 (62) Female  65 (38) Race White 133 (77) Black  23 (13) Other 17 (10) Histology squamous  63 (36) non-squamous 110 (64) Veri StratLabel Good 122 (71) Poor  51 (29) Performance 0  61 (35) Status 1 112(65) Disease Stage IIIB  8 (5) IV 165 (95) TreatmentCarboplatin/Paclitaxel/Cetuximab  68 (39) Carbo- orCisplatin/Gemcitabine/Cetuximab  69 (40) Carbo- orCisplatin/Pemetrexed/Cetuximab  36 (21) Age Median (range)  66 (35-86)

Deep MALDI spectra had been generated from these samples for a priorproject, but in an identical way to that described in Example 1, andthese were processed using identical procedures to those used indevelopment of the classifiers of Example 1. The full-set classifier ofExample 1 was applied to the resulting feature table, yielding aclassification of “Early” or “Late” for each sample. One hundred sixteensamples were classified as Early and the remaining 57 as Late. TheKaplan-Meier plot of overall survival for the cohort by classificationgroup is shown in FIGS. 25A and 25B and a summary of the analysis of OSand PPFS is given in table 35. Patient baseline characteristics aresummarized by classification group in table 36.

TABLE 35 Summary of the performance of the classifier on the ACORN NSCLCcohort HR log-rank Median #Early/#Late Endpoint (95% CI) p (Early, Late)116/57 OS 0.36 (0.28- <0.0001 7.0, 20.1 (months) 0.55) 116/57 PFS 0.60(0.44- 0.0017 3.9, 5.9 (months) 0.82)

TABLE 36 Baseline characteristics by classification of full-setclassifier of Example 1 Early Late n (%) n (%) Gender Male   74 (64) 34(60) Female   42 (36) 23 (40) Race White   86 (74) 47 (82) Black   19(16)  4 (7) Other   11 (9)  6 (11) Histology squamous   45 (39) 18 (32)non-squamous   71 (61) 39 (68) Veri Strat Label Good   65 (56) 57 (100)Poor   51 (44)  0 (0) Performance 0   34 (29) 27 (47) Status 1   82 (71)30 (53) Disease Stage IIIB   5 (4)  3 (5) IV  111 (96) 54 (95) TreatmentCarboplatin/Paclitaxel/   46 (40) 22 (39) Cetuximab Carbo- or Cisplatin/   46 (40) 23 (40) Gemcitabine/Cetuximab Carbo-or Ci splatin/  24 (21) 12 (21) Pemetrexed/Cetuximab Age Median (range) 66.5 (35-85)64 (46-86)FIGS. 26A and 26B shows the time-to-event outcomes broken down byclassification and VeriStrat label (using the classification algorithm,feature definitions, and NSCLC training set described in U.S. Pat. No.7,736,905). It is apparent that both VeriStrat groups within the Earlyclassification group have similarly poor outcomes.

Ovarian Cancer Cohort

A set of 165 samples from an observation trial of patients with ovariancancer were available. Patients underwent surgery followed byplatinum-based chemotherapy. Samples were taken at the time of surgery.Of the 165 patients, 23 did actually not start chemotherapy, were notnewly diagnosed, or had received prior therapy for ovarian cancer.Outcome data was not available for an additional four patients. Data arepresented here for the remaining 138 patients. The most importantbaseline clinical data available for these patients are summarized intable 37 and OS and disease-free survival (DFS) are shown in FIGS. 27Aand 27B. Note, two patients of the 138 did not have DFS available.

TABLE 37 Baseline characteristics of the ovarian cohort n (%) Histologyserous 100 (72) non-serous  38 (28) Veri Strat Label Good 110 (80) Poor 27 (20) Indeterminate  1 (1) FIGO 1  13 (9) 2  3 (2) 3  54 (39) 4  29(21) NA  39 (28) Histologic Grade NA  2 (1) 1  7 (5) 2  53 (38) 3  76(55) Metastatic Disease yes  20 (14) no 118 (86) Age Median (range)  59(18-88)

Deep MALDI spectra had been generated from these samples for a priorproject, in an identical manner as outlined in Example 1, and these wereprocessed using identical procedures to those used in development of thenivolumab test described in Example 1. The Example 1 full-set classifierwas applied to the resulting feature table, yielding a classification of“Early” or “Late” for each sample. Seventy six samples were classifiedas Early and the remaining 62 as Late. The Kaplan-Meier plots of overalland disease-free survival by classification are shown in FIGS. 28A and28B, and a summary of the analysis of OS and PFS is given in table 38.Patient baseline characteristics are summarized by classification groupin table 39.

TABLE 38 Summary of the performance of the full-set classifier ofExample 1 on the ovarian cancer cohort HR log-rank Median #Early/#LateEndpoint (95% CI) p (Early, Late) 76/62 OS 0.48 (0.30- 0.0021 35, notreached 0.76) (months) 74/62 PFS 0.48 (0.30- 0.0011 17, not reached0.73) (months)

TABLE 39 Baseline characteristics by classification produced fromfull-set classifier of Example 1 Early Late n (%) n (%) Histology serous61 (80)   39 (63) non-serous 15 (20)   23 (37) VeriStrat Label Good 48(63)   62 (100) Poor 27 (36)   0 (0) Indeterminate  1 (1)   0 (0) FIGO 1 2 (3)   11 (18) 2  2 (3)   1 (2) 3 32 (42)   22 (35) 4 22 (29)   7 (11)NA 18 (24)   21 (34) Histologic Grade NA  0 (0)   2 (3) 1  1 (1)   6(10) 2 32 (42)   21 (34) 3 43 (57)   33 (53) Metastatic yes 15 (20)   5(8) Disease no 61 (80)   57 (92) Age Median (range) 60 (35-88) 57.5(18-83)

FIGS. 29A and 29B show the time-to-event outcomes broken down byclassification and VeriStrat label (testing in accordance with U.S. Pat.No. 7,736,905). It is apparent that both VeriStrat groups Good and Poorwithin the Early classification produced by the full-set classifier ofExample 1 have similarly poor outcomes.

In summary, a testing method of making a prediction of whether anovarian or NSCLC patient is likely to benefit from chemotherapy, e.g.,platinum doublet chemotherapy, involves the following steps:

a) conducting mass spectrometry on a blood-based sample of the patientand obtaining mass spectrometry data;

(b) obtaining integrated intensity values in the mass spectrometry dataof a multitude of pre-determined mass-spectral features (such as forexamples the features listed in Appendix A or some subset thereof, e.g.,after a deselection of noisy features that do not significantlycontribute to classifier performance such as the features of one of thesets of Appendix B); and

(c) operating on the mass spectral data with a programmed computerimplementing a classifier (e.g., a classifier generated in accordancewith FIG. 8, the full-set classifier of Example 1).

In the operating step the classifier compares the integrated intensityvalues with feature values of a training set of class-labeled massspectral data obtained from blood-based samples obtained from amultitude of melanoma patients treated with an antibody drug blockingligand activation of programmed cell death 1 (PD-1), e.g., nivolumab,with a classification algorithm. The classifier generates a class labelfor the sample, wherein the class label “early” or the equivalentpredicts the patient is likely to obtain relatively less benefit and/orhave worse outcome from the chemotherapy and the class label “late” orthe equivalent indicates the patient is likely to obtain relativelygreater benefit and/or have better outcome from the chemotherapy. In oneembodiment the chemotherapy is platinum-doublet chemotherapy.

Additionally, preferably the mass spectral data is acquired from atleast 100,000 laser shots performed on the blood-based sample usingMALDI-TOF mass spectrometry.

The classifier is preferably obtained from a combination of filteredmini-classifiers using a regularized combination method.

A practical testing environment for conducting the test on ovarian andNSCLC cancer patients is described in FIG. 15 and the following section.

The following clauses are offered as further examples of the inventionsdisclosed in Example 4.

1. A method of predicting overall survival of a non-small cell lungcancer (NSCLC) or ovarian cancer patient treated with chemotherapy,comprising:

a) conducting mass spectrometry on a blood-based sample of the patientand obtaining mass spectrometry data;

(b) obtaining integrated intensity values of a multitude ofpre-determined mass-spectral features in the mass spectrometry data; and

(c) operating on the mass spectral data with a programmed computerimplementing a classifier;

wherein in the operating step the classifier compares the integratedintensity values with feature values of a reference set of class-labeledmass spectral data obtained from blood-based samples obtained from amultitude of melanoma patients treated with an antibody drug targetingprogrammed cell death 1 (PD-1) with a classification algorithm andgenerates a class label for the sample, wherein the class label “early”or the equivalent predicts the patient is likely to have a relativelyless benefit and/or worse outcome (survival) and the class label of“late” or the equivalent indicates the patient is likely to obtainrelatively greater benefit and/or better outcome (survival) from thechemotherapy.

2. The method of clause 1, wherein the chemotherapy comprises platinumdoublet chemotherapy.3. The method of clause 1, wherein the chemotherapy comprises thecombination of platinum doublet+cetuximab chemotherapy.4. The method of clause 1, wherein the pre-determined mass spectralfeatures include a multitude of features listed in Appendix A orAppendix B.5. The method of any of clauses 1-4, wherein the mass spectral data isacquired from at least 100,000 laser shots performed on the sample usingMALDI-TOF mass spectrometry.6. The method of any of clauses 1-5, wherein classifier is obtained froma filtered combination of miniClassifiers which are combined using aregularization procedure.7. The method of clause 6, wherein the mini-classifiers are filtered inaccordance with any one of the criteria listed in Table 10.8. The method of clause 6, wherein the classifier is obtained from amultitude of master classifiers generated from a multitude ofseparations of a development set of samples into a training set and atest set.9. A machine predicting overall survival of a non-small cell lung cancer(NSCLC) or ovarian cancer patient treated with chemotherapy, comprising:

a memory storing a reference set in the form of feature values for amultitude of mass spectral features obtained from mass spectra ofblood-based samples from a multitude of melanoma patients treated withan antibody drug blocking ligand activation of programmed cell death 1(PD-1);

the memory further storing a set of code defining a final classifierbased on a multitude of master classifiers, each master classifiergenerated from filtered mini-classifiers combined using a regularizedcombination method;

a central processing unit operating on the set of code and the referenceset and mass spectral data obtained from a blood-based sample of a NSCLCor ovarian cancer patient to be tested and responsively generating aclass label for the blood-based sample, wherein the class label “early”or the equivalent predicts the patient is likely to have a relativelyless benefit and/or worse survival and the class label of “late” or theequivalent indicates the patient is likely to obtain relatively greaterbenefit and/or better survival from the chemotherapy.

10. A laboratory test center comprising the machine of clause 9 and aMALDI-TOF mass spectrometer configured for conducting mass spectrometryon the blood-based sample from the NSCLC or ovarian cancer patient.11. A method of treatment of a NSCLC or ovarian cancer patient,comprising:

administering chemotherapy to the NSCLC or ovarian cancer patient,

wherein the patient has been previously selected for chemotherapy byperformance of the method of any one of clauses 1-9 on a blood-basedsample of the patient and the sample was assigned the class label oflate or the equivalent.

12. The method of clause 11, wherein the chemotherapy comprises platinumdoublet chemotherapy.13. The method of clause 11, wherein the cancer patient is a NSCLCpatient, and wherein the chemotherapy comprises platinum doublet pluscetuximab.

Example 5 Laboratory Test Center and Computer Configured as Classifier

Once the classifier as described in conjunction with Examples 1, 2, 3, 9(or the other Examples) has been developed, its parameters and referenceset can now be stored and implemented in a general purpose computer andused to generate a class label for a blood-based sample, e.g., inaccordance with the tests described in Examples 1, 2, 3 and 4. Dependingon the particular clinical question being asked (and the type of patientthe sample is obtained from), the class label can predict in advancewhether a melanoma or other cancer patient is likely to benefit fromimmune checkpoint inhibitors such as antibodies blocking ligandactivation of PD-1, such as nivolumab, or for example predict melanomaor other cancer patient benefit from antibodies blocking CTLA4, or ifdeveloped in accordance with Example 1, predict whether an ovarian orNSCLC cancer patient is likely to have better or worse overall survivalon chemotherapy.

FIG. 15 is an illustration of a laboratory testing center or system forprocessing a test sample (in this example, a blood-based sample from amelanoma, ovarian or NSCLC patient) using a classifier generated inaccordance with FIG. 8. The system includes a mass spectrometer 1506 anda general purpose computer 1510 having CPU 1512 implementing aclassifier 1520 coded as machine-readable instructions and a memory 1514storing reference mass spectral data set including a feature table 1522of class-labeled mass spectrometry data. This reference mass spectraldata set forming the feature table 1522 will be understood to be themass spectral data (integrated intensity values of predefined features,see Appendix A or Appendix B), associated with a development sample setto create the classifier of FIG. 8 and Examples 1-4. This data set couldbe from all the samples, e.g., for the full-set classifier of Example 1or a subset of the samples (e.g., development set of one half thesamples) plus a set of mass spectral data from NSCLC patients used todevelop the classifier of Example 2. It will be appreciated that themass spectrometer 1506 and computer 1510 of FIG. 15 could be used togenerate the classifier 1520 in accordance with the process of FIG. 8.

The operation of the system of FIG. 15 will be described in the contextof conducting a predictive test for predicting patient benefit ornon-benefit from antibodies blocking ligand activation of PD-1 asexplained above. The following discussion assumes that the classifier1520 is already generated at the time of use of the classifier togenerate a class label (Early or Late, or the equivalent) for a testsample. The method of operation of FIG. 15 for the other tests (benefitfrom anti-CTLA4 drugs, overall survival prediction on chemotherapy inovarian and NSCLC, etc.) is the same.

The system of FIG. 15 obtains a multitude of samples 1500, e.g.,blood-based samples (serum or plasma) from diverse cancer (e.g.,melanoma) patients and generates a class label for the sample as afee-for-service. The samples 1500 are used by the classifier 1520(implemented in the computer 1510) to make predictions as to whether thepatient providing a particular sample is likely or not likely to benefitfrom immune checkpoint inhibitor therapy. The outcome of the test is abinary class label such as Early or Late or the like which is assignedto the patient blood-based sample. The particular moniker for the classlabel is not particularly important and could be generic such as “class1”, “class 2” or the like, but as noted earlier the class label isassociated with some clinical attribute relevant to the question beinganswered by the classifier. As noted earlier, in the present context theEarly class label is associated with a prediction of relatively pooroverall survival, and the Late class label is associated with aprediction of relatively better (longer) overall survival on the immunecheckpoint inhibitor.

The samples may be obtained on serum cards or the like in which theblood-based sample is blotted onto a cellulose or other type card.Aliquots of the sample are spotted onto one or several spots of aMALDI-TOF sample “plate” 1502 and the plate inserted into a MALDI-TOFmass spectrometer 1506. The mass spectrometer 1506 acquires mass spectra1508 from each of the spots of the sample. The mass spectra arerepresented in digital form and supplied to a programmed general purposecomputer 1510. The computer 1510 includes a central processing unit 1512executing programmed instructions. The memory 1514 stores the datarepresenting the mass spectra 1508. Ideally, the sample preparation,spotting and mass spectrometry steps are the same as those used togenerate the classifier in accordance with FIG. 8 and Examples 1 and 2.

The memory 1514 also stores a data set representing classifier 1520,which includes a) a reference mass spectral data set 1522 in the form ofa feature table of N class-labeled spectra, where N is some integernumber, in this example a development sample set of spectra used todevelop the classifier as explained above or some sub-set of thedevelopment sample set (e.g., DEV1 or DEV2 above in Example 1, or all ofthe 119 samples). The classifier 1520 includes b) code 1524 representinga kNN classification algorithm (which is implemented in themini-classifiers as explained above), including the features and depthof the kNN algorithm (parameter s) and identification of all themini-classifiers passing filtering, c) program code 1526 for executingthe final classifier generated in accordance with FIG. 8 on the massspectra of patients, including logistic regression weights and datarepresenting master classifier(s) forming the final classifier,including probability cutoff parameter, mini-classifier parameters foreach mini-classifier that passed filtering, etc., and d) a datastructure 1528 for storing classification results, including a finalclass label for the test sample. The memory 1514 also stores programcode 1530 for implementing the processing shown at 1550, including code(not shown) for acquiring the mass spectral data from the massspectrometer in step 1552; a pre-processing routine 1532 forimplementing the background subtraction, normalization and alignmentstep 1554 (details explained above), filtering and averaging of the 800shot spectra at multiple locations per spot and over multiple MALDIspots to make a single 100,000+shot average spectrum (as explainedabove) a module (not shown) for calculating integrated intensity valuesat predefined m/z positions in the background subtracted, normalized andaligned spectrum (step 1556), and a code routine 1538 for implementingthe final classifier 1520 using the reference dataset feature table 1522on the values obtained at step 1556. The process 1558 produces a classlabel at step 1560. The module 1540 reports the class label as indicatedat 1560 (i.e., “Early” or “Late” or the equivalent).

The program code 1530 can include additional and optional modules, forexample a feature correction function code 1536 (described in U.S.patent application publication 2015/0102216) for correcting fluctuationsin performance of the mass spectrometer, a set of routines forprocessing the spectrum from a reference sample to define a featurecorrection function, a module storing feature dependent noisecharacteristics and generating noisy feature value realizations andclassifying such noisy feature value realizations, modules storingstatistical algorithms for obtaining statistical data on the performanceof the classifier on the noisy feature value realizations, or modules tocombine class labels defined from multiple individual replicate testingof a sample to produce a single class label for that sample. Still otheroptional software modules could be included as will be apparent topersons skilled in the art.

The system of FIG. 15 can be implemented as a laboratory test processingcenter obtaining a multitude of patient samples from oncologists,patients, clinics, etc., and generating a class label for the patientsamples as a fee-for-service. The mass spectrometer 1506 need not bephysically located at the laboratory test center but rather the computer1510 could obtain the data representing the mass spectra of the testsample over a computer network.

Example 6 Correlation of Protein Functional Groups with ClassificationGroups and Mass Spectral Features

When building tests using the procedure of FIG. 8, it is not essentialto be able to identify which proteins correspond to which mass spectralfeatures in the MALDI TOF spectrum or to understand the function ofproteins correlated with these features. Whether the process produces auseful classifier depends entirely on classifier performance on thedevelopment set and how well the classifier performs when classifyingnew sample sets. However, once a classifier has been developed it may beof interest to investigate the proteins or function of proteins whichdirectly contribute to, or are correlated with, the mass spectralfeatures used in the classifier. In addition, it may be informative toexplore protein expression or function of proteins, measured by otherplatforms, that are correlated with the test classification groups.

Appendix K to our prior provisional application Ser. No. 62/289,587 setsforth the results of an analysis aimed at associating protein functionwith classification groups of the Example 1 and Example 2 classifiersand the mass spectral features measured in Deep MALDI spectra. InAppendix K, the nomenclature “IS2” corresponds to the full-set approach1 classifier of Example 1, and “IS4” corresponds to the classifier ofExample 2. A summary of the pertinent details of the methods we used andthe results we found are set forth in this Example. The discoveries wemade lead to new examples of how the classifiers of this disclosure canbe characterized and generalization of the classifiers to other immunecheckpoint inhibitors and other cancer indications beyond melanoma.

The data we used for the study include the feature table created duringthe application of the Example 1 full-set approach 1 classifier on a setof 49 serum samples (“Analysis Set”) composed of patients with cancerand some donors without cancer. This is a table of feature values foreach of the 59 features used in the Example 1 whole set approach 1classifier (see Appendix B) and 292 other features (see Appendix A) notused in the Example 1 full-set approach 1 classifier. The feature valueswere obtained from MALDI-TOF mass spectra obtained using the fullyspecified spectral acquisition and spectral processing processes definedin the Example 1 description for each of the 49 samples. We also usedthe list of classifications (Early/Late) obtained for the 49 samples inthe Analysis Set produced by the Example 1 full-set approach 1classifier. We also used the list of classifications (Early/Late)obtained for the 49 samples in the Analysis Set produced by the Example2 classifier. We also used a table of 1129 protein/peptide expressionmeasurements obtained from running a SomaLogic 1129 protein/peptidepanel on the Analysis Set.

We used a method known as Gene Set Enrichment Analysis (GSEA) applied toprotein expression data. Background information on this method is setforth in Mootha, et al., PGC-1α-responsive genes involved in oxidativephosphorylation are coordinately downregulated in human diabetes. NatGenet. 2003; 34(3):267-73 and Subramanian, et al., Gene set enrichmentanalysis: A knowledge-based approach for interpreting genome-wideexpression profiles. Proc Natl Acad Sci USA 2005; 102(43): 15545-50, thecontent of which are incorporated by reference herein. Specific proteinsets were created based on the intersection of the list of SomaLogic1129 panel targets and results of queries from GeneOntology/AmiGO2 andUniProt databases.

The implementation of the GSEA method was performed using Matlab.Basically, in our method we evaluated the correlation r betweenindividual proteins and group labels (Early and Late). Once thesecorrelations had been calculated for each protein, we ranked theproteins by value of r from largest to smallest, with larger values of rindicating greater correlation, and a value of r=0 meaning nocorrelation was found. We then calculated an enrichment score (ES) (asexplained in the Subramanian et al. paper above), which is designed toreflect the degree to which elements of a particular protein set areover-represented at the top or bottom of the ranked list of proteins. Weconsidered two possible definitions for the enrichment score, thedetails of which are set forth at page 5 of Appendix K of our priorprovisional application Ser. No. 62/289,587. We also calculated thecorresponding p value for the proteins, in order to assess thesignificance of the deviation of the calculated enrichment score fromits average value for a random distribution. We also calculated arunning sum (RS), as part of the calculation of the enrichment score,the details of which are explained in Appendix K of our priorprovisional application Ser. No. 62/289,587.

The results for the correlation of the protein sets with the Example 1full-set approach 1 classifier class labels (Early or Late) of the 49samples are shown in table 40.

TABLE 40 Results of GSEA applied to protein sets and Example 1 classlabels of the Analysis Set Definition 1 of Definition 2 of enrichmentscore enrichment score ES p ES p definition1 value definition2 valueAcute inflammatory 0.424 0.02 0.480 0.03 response Activation of innate0.412 0.55 0.518 0.56 immune response Regulation of adaptive −0.234 0.900.338 0.95 immune response Positive regulation of −0.495 0.29 0.673 0.21glycolytic process Immune T-cells −0.156 0.97 0.274 0.96 Immune B-cells0.213 0.91 0.312 0.95 Cell cycle regulation −0.207 0.81 0.371 0.50Natural killer regulation −0.406 0.39 0.429 0.68 Complement system 0.5520.01 0.565 0.02 Acute response 0.539 0.10 0.700 0.02 Cytokine activity−0.231 0.68 0.342 0.74 Wound healing −0.373 0.11 0.476 0.11 Interferon−0.178 0.94 0.330 0.84 Interleukin-10 0.190 0.77 0.332 0.64 Growthfactor receptor −0.221 0.45 0.309 0.84 signaling Immune Response Type 1−0.402 0.56 0.506 0.71 Immune Response Type 2 0.511 0.48 0.552 0.84Acute phase 0.572 0.01 0.693 <0.01 Hypoxia −0.247 0.65 0.363 0.71 Cancer0.153 0.96 0.298 0.80There are correlations at the p<0.05 level of the class labels with theprotein sets corresponding to the following biological processes: acuteinflammatory response, acute phase, and complement system. Correlationswith p values around 0.1 were found for the wound healing protein set.The correlation for the acute response has a p value of 0.02. We thenused statistical methods (see Appendix K of our prior provisionalapplication Ser. No. 62/289,587) for identifying subsets of proteins ofthe complement system, acute phase, acute response, and acuteinflammatory response protein sets that are most important for thesecorrelations, the results of which are shown in Tables 41A, 41B, 41C and41D, respectively.

TABLE 41A Proteins included in the extended leading edge set forcomplement (Amigo9). * indicates proteins to the right of the minimum ofRS and † indicates proteins with anti- correlations of at least as greatmagnitude as that at the maximum of RS. UniProtID Protein NameCorrelation P value P01024 Complement C3b   0.626 <0.01 P02741C-reactive protein   0.582 <0.01 P02748 Complement C9   0.559 <0.01P01024 Complement C3a anaphylatoxin   0.556 <0.01 P01024 Complement C3  0.525 <0.01 P11226 Mannose-binding protein C   0.461 <0.01 P06681Complement C2   0.418 0.01 P12956 ATP-dependent DNA helicase II 70   kDa subunit   0.412 0.01 P01031 Complement C5a   0.394 0.02 P02743 Serumamyloid P   0.391 0.02 P07357 P07358 Complement C8   0.377 0.03 P07360   P01031 P13671 Complement C5b,6 Complex   0.350 0.04 P01031 ComplementC5   0.337 0.05 P00751 Complement factor B   0.323 0.05 P01024Complement C3b, inactivated   0.323 0.05 P05155 C1-Esterase Inhibitor  0.313 0.06 P00736 Complement C1r   0.310 0.07 P13671 Complement C6  0.310 0.07 P48740 Mannan-binding lectin serine   0.283 0.09 peptidase1 P16109 P-Selectin −0.475*† <0.01 Q6YHK3 CD109 −0.364† 0.03

TABLE 41B Proteins included in the extended leading edge set for acutephase (UniProt1). * indicates proteins to the right of the minimum of RSand † indicates proteins with anti- correlations of at least as greatmagnitude as that at the maximum of RS. UniProtID Protein NameCorrelation P value P01009 alpha1-Antitrypsin   0.801 <0.01 P0DJI8 Serumamyloid A   0.704 <0.01 P18428 Lipopolysaccharide-binding protein  0.640 <0.01 Q14624 Inter-alpha-trypsin inhibitor heavy   0.603 <0.01chain H4    P02741 C-reactive protein   0.582 <0.01 P02671 P02675D-dimer   0.529 <0.01 P02679    P11226 Mannose-binding protein C   0.461<0.01 P00738 Haptoglobin   0.455 <0.01 P02743 Serum amyloid P   0.3910.02 P02765 alpha2-HS-Glycoprotein −0.593*† <0.01 P02787 Transferrin−0.502*† <0.01 P08697 alpha2-Antiplasmin −0.347*† 0.04 P08887Interleukin-6 receptor alpha chain −0.347*† 0.04

TABLE 41C Proteins included in the extended leading edge set for acuteresponse (Amigo11). * indicates proteins to the right of the minimum ofRS and † indicates proteins with anti- correlations of at least as greatmagnitude as that at the maximum of RS. UniProtID Protein NameCorrelation P value P18428 Lipopolysaccharide-binding protein   0.640<0.01 Q14624 Inter-alpha-trypsin inhibitor heavy   0.603 <0.01 chain H4   P05155 C1-Esterase Inhibitor   0.313 0.06 P13726 Tissue Factor  0.306 0.07 P48740 Mannan-binding lectin serine   0.283 0.09 peptidase1    P05231 Interleukin-6   0.266 0.11 P02765 alpha2-HS-Glycoprotein−0.593*† <0.01

TABLE 41 D Proteins included in the extended leading edge set for acuteinflammatory response (Amigo1). * indicates proteins to the right of theminimum of RS and † indicates proteins with anti-correlations of atleast as great magnitude as that at the maximum of RS. UniProtID ProteinName Correlation P value P01009 alphal-Antitrypsin   0.801 <0.01 Q14624Inter-alpha-trypsin inhibitor heavy   0.603 <0.01 chain H4    P02741C-reactive protein   0.582 <0.01 P01024 Complement C3a anaphylatoxin  0.556 <0.01 P01024 Complement C3   0.525 <0.01 P10600 Transforminggrowth factor beta-3   0.498 <0.01 Q00535 Q15078 Cyclin-dependent kinase5: activator   0.492 <0.01 p35 complex    P07951 Tropomyosin beta chain  0.478 <0.01 P02679 Fibrinogen gamma chain dimer   0.475 <0.01 P11226Mannose-binding protein C   0.461 <0.01 P00738 Haptoglobin   0.455 <0.01P12956 ATP-dependent DNA helicase II 70   0.412 0.01 kDa subunit   P02743 Serum amyloid P   0.391 0.02 P07357 P07358 Complement C8   0.3770.03 P07360    P06744 Glucose phosphate isomerase   0.364 0.03 P06400Retinoblastoma 1   0.340 0.04 P01031 Complement C5   0.337 0.05 P08107Hsp70   0.306 0.07 Q9Y552 Myotonic dystrophy protein kinase-   0.2900.09 like beta    Q8NEV9 Q14213 Interleukin-27   0.290 0.09 P05231Interleukin-6   0.266 0.11 P01019 Angiotensinogen   0.263 0.12 P02765alpha2-HS-Glycoprotein −0.593*† <0.01 O00626 Macrophage-derivedchemokine −0.535*† <0.01 P02649 Apolipoprotein E −0.421† 0.01 P08697alpha2-Antiplasmin −0.347† 0.04 P08887 Interleukin-6 receptor alphachain −0.347† 0.04 P08514 P05106 Integrin alpha-IIb: beta-3 complex−0.303† 0.07 Q9BZR6 Nogo Receptor/reticulon 4 receptor −0.300† 0.08P00747 Angiostatin −0.276† 0.10We further investigated the interaction of the processes related to theontologies common to the groups of proteins we identified from RS plots.We found that the processes are related to the complement activation,activation and regulation of the immune system, as well as innate immuneresponse and inflammatory response. Charts showing these relationshipsare found in Appendix K of our prior provisional application Ser. No.62/289,587. Appendix K, FIG. 8 also shows “heat maps” (i.e., plots of pvalues generated by GSEA analysis associating mass spectral featureswith protein expression values for all the m/z features used by theclassifier of Example 1, Appendix B), which demonstrate the results ofthe correlation of the protein sets with the mass spectral features usedfor the Example 1 full-set approach 1 classifier. See also FIGS. 45A and45B and the discussion thereof later in this document. We discoveredthat many of the mass spectral features used in the Example 1 classifierare related to the following biological processes: (1) acute phase, (2)acute response, (3) complement system, and (4) acute inflammatoryresponse. Very few of the mass spectral features used are associatedwith the specific immune-related protein functions we investigated(i.e., “activation of innate immune response”, “regulation of adaptiveimmune response”, “immune T-cells”, “immune B-cells”, “interferon”,“interleukin-10”). These relationships are demonstrated by the heat mapsof FIG. 8 of Appendix K of our prior provisional application Ser. No.62/289,587, with darker areas associated with lower p values and thushigher correlation between protein and class label. FIG. 8 shows theheat maps for two different definitions of the enrichment score, but theplots are similar. See also FIGS. 45A and 45B of this document and theassociated discussion below.

We performed the same analysis of the correlation between the proteinsets with the class labels produced by the classifier of Example 2. Theresults are shown in Table 42. Note that the proteins associated withthe following biological processes are strongly correlated with theclass labels: acute inflammatory response, complement system, acuteresponse and acute phase. In addition, proteins associated with ImmuneResponse Type 2 were also strongly correlated with the class labels.

TABLE 42 Results of GSEA applied to protein sets and Example 2 classlabels of the Analysis Set Definition 1 Definition2 ES p ES pdefinition1 value definition2 value Acute inflammatory response 0.451<0.01 0.487 0.03 Activation of innate immune response 0.511 0.33 0.5110.58 Regulation of adaptive immune 0.173 0.99 0.332 0.96 responsePositive regulation of glycolytic −0.328 0.80 0.446 0.91 process ImmuneT-cells −0.181 0.91 0.323 0.81 Immune B-cells 0.404 0.22 0.418 0.65 Cellcycle regulation −0.178 0.95 0.338 0.75 Natural killer regulation −0.2410.87 0.255 0.10 Complement system 0.629 <0.01 0.633 <0.01 Acute response0.535 0.12 0.688 0.03 Cytokine activity −0.224 0.71 0.401 0.35 Woundhealing −0.324 0.23 0.499 0.06 Interferon 0.201 0.86 0.319 0.88Interleukin-10 0.306 0.05 0.369 0.31 Growth factor receptor signaling−0.199 0.66 0.345 0.55 Immune Response Type 1 −0.227 0.98 0.386 0.97Immune Response Type 2 0.852 <0.01 0.871 0.04 Acute phase 0.659 <0.010.752 <0.01 Hypoxia 0.242 0.68 0.408 0.45 Cancer 0.154 0.95 0.273 0.96There are correlations at the p<0.05 level of the class labels with theprotein sets corresponding to acute inflammatory response, acuteresponse, acute phase, complement system, interleukin-10 (with ESdefinition 1), and immune response type 2. Correlations with p valuesbelow 0.1 were also found for the wound healing protein set.

We further identified proteins from the complement system, acute phase,acute response, acute inflammatory response, and interleukin-10, andimmune response type 2 processes important for the Example 2 classifier.The results are listed in Tables 11A-11F of Appendix K of our priorprovisional application Ser. No. 62/289,587. Many of the proteins listedin Tables 11A-11D are found in the Tables 41A-41D. We also calculatedthe correlations of the protein sets with the mass spectral featuresused in Example 2 classifier and produced heat maps, see FIGS. 10 and 11of Appendix K of our prior provisional application Ser. No. 62/289,587.Many mass spectral features used in the Example 2 classification arerelated to the complement system and acute inflammation/acute phasereaction. However, these biological processes are somewhat less dominantas compared to the mass spectral features used in the Example 1 full-setapproach 1 classifier. Table 12 of Appendix K of our prior provisionalapplication Ser. No. 62/289,587 shows the number of mass spectralfeatures (out of the 351 listed in Appendix A) which are associated withthe protein sets at various significance levels (p values). In general,there are more mass spectral features associated with the proteins ofthe acute inflammation, complement system, acute response, acute phase,immune response type 2, and wound healing processes as compared to otherprocesses.

The above discussion, and the report of Appendix K of our priorprovisional application Ser. No. 62/289,587, demonstrates that thecombination of Deep MALDI measurements with simple modifications of GSEAshows promise for extracting useful information on the biologicalfunctions related to our test labels of this disclosure. It also allowsus to gain insight into the functions related to the mass spectralfeatures (Deep MALDI peaks) that we measure from serum samples.

The protein functions associated with the mass spectral features used inthe Example 1 full-set approach 1 classifier were consistent with theprotein functions associated with the class labels, namely acute phasereactants and the complement system. These functions are also consistentwith the functions of the available protein IDs of the features used inthe same Example 1 classifier (Table 17, Example 1). Other plausiblebiological functions did not show any significant association with theclass labels. This does not imply that these other functions are notrelevant for the biology of immunotherapies; it just means that we haveno evidence that they play a major role in the classifications producedby the full-set approach 1 classifier of Example 1. However, although wemeasured features (proteins) related to most of these other functions,they were not used in our tests, and the test classifications were notsignificantly associated with these protein functions.

In the case of the Example 2 classifier, we saw that the strength ofassociation of classification with acute phase and complement functionsincreased quite significantly compared with Example 1, which may be anindication that the classifier of Example 2 is a “cleaner” test, lessconfounded by prognostic effects. We also observed that IL-10 relatedfunctions are associated with the Example 2 class groups at the p=0.05significance level.

There are limitations with these data caused mainly by the limited sizeof the Analysis Set of samples, resulting in fairly wide nulldistributions, and hence limited statistical power. The simplest way toimprove on this would be to have paired Deep MALDI/Somalogic data onmore samples. This would also allow us to have an independent validationof these present results. While the number of proteins in the Somalogicpanel is rather large, one could also consider using additional orextended panels.

Due to computer resource limitations we did not perform a falsediscovery rate analysis wrapped around the protein sets. Such ananalysis would also require further theoretical work to assess theapplicability of, and possibly work to improve on, the suggested setnormalizations in the Subramanian paper. While this should in principlebe done, the observed effects, especially for some of the mass spectralfeatures, are so clear and large that we do not expect any substantialqualitative changes to the main conclusions of this analysis.

Because it is well-known that many proteins in circulation are relatedto acute phase reactants and the complement system, it may be notsurprising that these two functions appear associated with many of themass spectral features we measure with Deep MALDI. However, we did seeother significant correlations, especially in the case of the Example 2classifier, indicating that our results are not a trivial reflection ofabundance of circulating proteins. In addition, within the subset offeatures used in the full-set approach 1 Example 1 classifier (seeAppendix B), the proportion of features associated with acute phase andthe complement system was substantially higher than that observed in thewhole set of 351 Deep MALDI features listed in Appendix A.

The above discoveries can be used to build a classifier used in guidingimmune checkpoint inhibitor treatment for a cancer patient. Inparticular, such a classifier includes a memory storing a reference setof class-labelled mass spectral data obtained from blood-based samplesof melanoma patients treated with an immune checkpoint inhibitor agent.The mass spectral data is in the form of feature values for a multitudeof mass spectral features, wherein the mass spectral features areidentified with proteins circulating in serum associated with at leastthe following biological processes: (1) acute phase, (2) acute response,(3) complement system, and (4) acute inflammatory response. See theabove discussion and the heat maps of FIGS. 8, 10 and 11 of Appendix Kof our prior provisional application Ser. No. 62/289,587. The classifierfurther includes a programmed computer (see FIG. 15) implementing aclassification algorithm on a set of mass spectral data includingfeature values for the multitude of mass spectral features obtained froma test blood-based sample and the reference set and generating a classlabel for the test blood-based sample. Alternatively, the mass spectralfeatures may further include features associated with immune responsetype 2 and interleukin-10 processes. In one embodiment, the featuresinclude the features listed in Appendix A, Appendix B or Appendix C.

Furthermore, application of GSEA methods to the data obtained using theSomaLogic panel in combination with the results of full-set approach 1classifier of Example 1 and further analysis allowed us to correlate up-and down-regulation of proteins with Early and Late classifications. Inparticular, proteins in Tables 41 A-D with positive correlationcoefficient, are correlated with up-regulation in samples classified asEarly.

Further analysis of the running sum of the Complement protein setallowed identifying proteins that have the biggest impact on thecorrelation of the corresponding proteins sets with the classificationresults (Table 5, Appendix K of our prior provisional application Ser.No. 62/289,587). Table 43 below lists proteins which have P-value forcorrelation with classification labels ≦0.05 and are included in Group1(leading edge) of the Complement protein set.

TABLE 43 Proteins associated with gene ontology “Complement system” andcorrelated with full-set approach 1 classifier of Example 1. CorrelatedExpression UniProt ID Full Name in the “Early” group P02741 C-reactiveprotein Up P00736 Complement C1r Up P01024 Complement C3 Up P01024Complement C3a anaphylatoxin Up P01024 Complement C3b Up P01031Complement C5 Up P01031 Complement C5a Up P01031 Complement C5b,6Complex Up P07357 Complement C8 Up P02748 Complement C9 Up P00751Complement factor B Up P01024 Complement C3b, inactivated Up P16109P-Selectin Down P02743 Serum amyloid P Up P06681 Complement C2 Up P12956ATP-dependent DNA helicase II 70 kDa subunit Up P11226 Mannose-bindingprotein C Up Q6YHK3 CD109 DownOne can see that the group classified as Early is characterized byup-regulation of most of the components of the complement system, whichraises the question of possible biological relationship between thisup-regulation and unfavorable prognosis of patients classified as Early,as well as their little benefit from PD-1 inhibitors and, possibly,other types of immunotherapy.

A growing body of evidence suggests a complex role for the complementsystem in tumorigenesis, which, depending on an intricate balance ofmultiple factors, can be pro- or anti-tumor. Neoplastic transformationis accompanied by an increased capacity to activate complement. Pio, R.,Corrales, L. & Lambris, J. D. The role of complement in tumor growth.Adv Exp Med Biol 772, 229-62 (2014). Activated complement proteins havebeen shown to inhibit tumor growth by promoting complement-dependentcytotoxicity (Janelle, V. & Lamarre, A. Role of the complement system inNK cell-mediated antitumor T-cell responses. Oncoimmunology 3, e27897(2014)) and inhibition of Treg function. Mathern, D. R. & Heeger, P. S.Molecules Great and Small: The Complement System. Clin J Am Soc Nephrol10, 1636-50 (2015).

On the other hand, recent data have demonstrated that activatedcomplement proteins, interacting with the components of the innate andadaptive immune systems, can promote carcinogenesis. For example, inmouse models of melanoma it was shown that “decomplementation” led to arobust antitumor CD8+ response and improved cytotoxic activity of NKcells, while activated complement system resulted in limitedaccumulation of tumor-specific cytotoxic T cells (CTLs), and, at thesame time, promoted tumor infiltration with immunosuppressivemyeloid-derived suppressor cells (MDSc), which suppress NK- and T-cellfunctions. Janelle, V. et al. Transient complement inhibition promotes atumor-specific immune response through the implication of natural killercells. Cancer Immunol Res 2, 200-6 (2014). Similarly, complementactivation and C5a signaling were shown to be associated withrecruitment of MDSCs into tumors, suppression of effector CD8+ and CD4+T cells, generation of regulatory T cells (Tregs), Th2 predominantimmune responses, and facilitation of lung and liver metastasis inmodels of breast and cervical cancer. Markiewski, M. M. et al.Modulation of the antitumor immune response by complement. Nat Immunol9, 1225-35 (2008); Vadrevu, S. K. et al. Complement c5a receptorfacilitates cancer metastasis by altering T-cell responses in themetastatic niche. Cancer Res 74, 3454-65 (2014). C5a was shown topromote differentiation of Tregs, causing inhibition on antitumoractivity. Gunn, L. et al. Opposing roles for complement component C5a intumor progression and the tumor microenvironment. J Immunol 189, 2985-94(2012). Complement can assist the escape of tumor cells fromimmunosurveillance, support chronic inflammation, promote angiogenesis,activate mitogenic signaling pathways, sustain cell proliferation andinsensitivity to apoptosis, and participate in tumor invasion andmigration. Pio et al., supra. In lung cancer models, blockade of C5asignaling led to the inhibition of key immunosuppressive moleculeswithin the tumor. These molecules included IL-10, IL-6, CTLA4, LAF3, andPDL18.

The latter findings have direct implications for the activity of theimmune checkpoint inhibitors in cancer patients, and, consequently, forthe role of our Example 1 and Example 2 classifiers. In particular, theobserved upregulation of the complement system proteins in the groupclassified as Early may indicate that these patients have higher levelsof immunosuppression, and/or higher levels of pro-tumor inflammation,related to the activation of the corresponding immune checkpoints, andas a result are less responsive to such drugs as nivolumab, ipilimumab,pembrolizumab, or other agents targeting these pathways. Interestingly,it has been shown that the complement protein C5a promotes theexpression of the PD-1 ligands, PD-L1 and PD-L2. Zhang, J. Immunol.2009; 182: 5123-5130. In this scenario one could envision that excessivecomplement upregulation might compete with efforts to inhibit PD-1. Onthe other hand, the results of recent clinical trials suggest thatpatients with tumor microenvironment characterized by high expression ofPDL1 and presence of Tregs are more likely to respond to anti-PD-1,anti-CTLA4, or high dose IL-2 therapy. Though we do not know how exactlyupregulation of the complement system is correlated with Example 1 andExample 2 classifications, this connection is in line with thebiological effects discussed above.

Consequently, we can expect that Example 1 and Example 2 classifiers maybe relevant for the broad variety of drugs affecting the immunologicalstatus of the patient, such as various immune checkpoint inhibitors,high dose IL-2, vaccines, and/or combinational therapy. Furthermore,since effects that are measured in serum reflect the organism status asa whole, and the complement system affects innate and adaptive immunityon the global level, not just in a tumor site, the classifiers areexpected to have similar performance in different indications in cancer(e.g., lung, renal carcinoma), and are not restricted to melanoma.

Another large gene ontology protein set correlated with Example 1 andExample 2 Early and Late classifications is “Acute inflammatoryresponse”. Proteins correlated with these classification groups fromthis set (p≦0.05) are presented in Table 44.

TABLE 44 Proteins associated with gene ontology “Acute Inflammatoryresponse” and correlated with full set approach 1 Example 1classification. Correlated Expression UniProtID Protein Name in the“Early” group P01009 alpha1-Antitrypsin Up Q14624 Inter-alpha-trypsininhibitor heavy chain H4 Up P02741 C-reactive protein Up P01024Complement C3a anaphylatoxin Up P01024 Complement C3 Up P10600Transforming growth factor beta-3 Up Q0053, Q15078 Cyclin-dependentkinase 5:activator p35 complex Up P07951 Tropomyosin beta chain UpP02679 Fibrinogen gamma chain dimer Up P11226 Mannose-binding protein CUp P00738 Haptoglobin Up P12956 ATP-dependent DNA helicase II 70 kDasubunit Up P02743 Serum amyloid P Up P07357, Complement C8 Up P07358,P07360, P06744 Glucose phosphate isomerase Up P06400 Retinoblastoma 1 UpP01031 Complement C5 Up P01019 Angiotensinogen Down P02765alpha2-HS-Glycoprotein Down O00626 Macrophage-derived chemokine DownP02649 Apolipoprotein E Down P08697 alpha2-Antiplasmin Down P08887Interleukin-6 receptor alpha chain DownIt is generally accepted that cancer triggers an intrinsic inflammatoryresponse that creates a pro-tumorigenic microenvironment (Mantovani, A.,Allavena, P., Sica, A. & Balkwill, F. Cancer-related inflammation.Nature 454, 436-44 (2008)); “smouldering” inflammation is associatedwith most, if not all, tumors and supports their progression. Porta, C.et al. Cellular and molecular pathways linking inflammation and cancer.Immunobiology 214, 761-77 (2009). Inflammation is intrinsicallyassociated with the complement system, and complement system promotestumor growth in the context of inflammation. Hence, it seems logicalthat both systems came out as significantly correlated with Example 1and Example 2 classifications.

Tumor associated inflammatory response can be initiated and/or modulatedby cancer therapy. On one hand, it can have tumor-promoting functions,but on the other hand it can enhance presentation of tumor-antigens andsubsequent induction of anti-tumor immune response. Grivennikov, S. I.,Greten, F. R. & Karin, M. Immunity, inflammation, and cancer. Cell 140,883-99 (2010). While a T-cell inflamed microenvironment, characterizedby recruiting of CD8+ and CD4+ lymphocytes to the tumor, is considered anecessary condition for effective immunotherapeutic treatment,activation of the elements of the acute inflammatory pathway is likelycorrelated with the negative prognosis. The exact mechanisms of actionremain poorly understood, but our data on upregulation of this system inthe Early group seem to be consistent with the existing clinical data.

The present disclosure demonstrates how it is possible to incorporatebiological insight into a classifier development exercise, such as forexample the approach of FIGS. 8A and 8B, using mass spectrometry data.The present disclosure also demonstrates how it is possible to testbiologically motivated hypotheses about the relevance of biologicalfunctions for certain disease states using mass spectral data. This isachieved by using gene set enrichment analysis to associate biologicalfunctions, via subsets of proteins related to these functions, withfeatures (peaks) in mass spectra obtained from serum samples, and usingsuch identified features to train a classifier with the aid of acomputer.

In particular, classifier development and training methods are disclosedwhich make use of mass spectrometry of a development set of samples.Protein expression data are obtained from a large panel of proteinsspanning biological functions of interest either for each of the samplesin the development set of samples or, alternatively and more typically,for each of the samples in another sample cohort for which massspectrometry data are also available. The latter case is preferredbecause the measurement of abundance of many proteins via a proteinassay requires a large amount of sample and is expensive and timeconsuming. It is also not necessary to construct the relation betweenmass spectral peaks and biological function for every developmentproject because we can infer the correlation of mass spectral featuresto function from any reference set that has sufficient protein coverage.In the following we exemplify our methods using these two optionsinterchangeably.

In our method, we identify statistically significant associations of oneor more of the mass spectral features with sets of proteins grouped bytheir biological function. With the aid of a computer, we then use theseone or more mass spectral features that were identified (and typically10-50 of such features) to train a classifier. This training may takethe form of a classifier development exercise, one example of which isshown in FIG. 8 and described in detail previously. The classifier is inthe form of a set of parameters and associated program instructions orcode, which when executed by a computer assigns a class label to massspectrometry data of a sample of the same type as the development set ofsamples in accordance with the programmed instructions.

As described in this Example, using Gene Set Enrichment Analysis (GSEA)methods, it is possible to look for statistically significantassociations of mass spectral features with sets of proteins grouped bytheir biological function (“protein functional groups”) avoiding directprotein identification, and taking advantage of the high-throughputaspect of mass spectrometry. A system or set of components that conductsGSEA and identifies such mass spectral features with a particularprotein functional group or subset is referred to herein as a “platform”or “GSEA platform.” Such a platform consists of both a known,conventional protein expression assay system (e.g., the SOMAscan assayprovided by SomaLogic of Boulder Colo.) and a computer for implementingGSEA analytical procedures to identify the mass spectra featuresassociated with functional groups of proteins as described in thisdocument.

It is necessary to have matched mass spectral data (preferably from ahigh sensitivity method such as “Deep MALDI” see U.S. Pat. No.9,279,798, the content of which is incorporated by reference herein) andprotein expression data from a large panel of proteins spanningbiological functions of interest on a single set of serum samples. Usingwell-known protein databases, such as UniProt or GeneOntology/AmiGO2,subsets of proteins from the universe of measured proteins can bedefined based on their biological functions. The entire list of measuredproteins is first ranked according to the correlation of each proteinwith the mass spectral feature of interest. The GSEA method then looksfor over- or under-representation of the proteins included in aparticular protein functional subset as a function of rank in thisranked list of all measured proteins and provides a way of assessing itsstatistical significance. Thus, the association of the mass spectralfeature of interest with different protein functional groups can beassessed. This procedure can be repeated for as many spectral featuresand as many protein functional groups as desired. Setting a cutoff onthe degree of association or the p value for significance of theassociation, all mass spectral features associated with a particularprotein functional subset can be identified. This set of mass spectralfeatures is then used to train a classifier, such as a k-NearestNeighbor (kNN) classifier.

One method we prefer for classifier training and development is known asDiagnostic Cortex, which is described at length previously in thecontext of FIG. 8. It has been demonstrated that using the DiagnosticCortex classifier development procedure, clinical data and Deep MALDImass spectrometry data can be combined to produce clinically usefulmolecular diagnostic tests. One advantage of this method is that itallows for the design and tuning of tests to meet required standards ofclinical utility. The use of subsets of functionally related massspectral features (identified from the procedures mentioned in theprevious paragraph) instead of all available mass spectral data providesan additional option for test design and optimization. The mass spectralfeatures associated with one or more protein functional subsets can beselected and combined with the clinical data of a set of samples tocreate a new classifier and associated test, allowing for theinvestigation of the relevance of individual biological functions orgroups of biological functions for the required classification task.

We also describe creation of a multitude of different classifiers usingdifferent feature subsets related to different protein functional groupsand combine them, for example by simple majority vote, more complexensemble averaging, or some rule-based system, to produce an overallclassification that combines the information content across variousbiological functions. In one variation of this, we can look at thefunctional subsets becoming relevant on the groups defined by theclassifier after taking out, or taking care of the main effects, byusing the peaks related to the functional groups in the classifier, andusing these newly relevant peaks for building a new classifier in termsof a hierarchy of biological functions. For example, we can use peaksassociated with acute response function to train a first levelclassifier. We train a second classifier using a set of peaks associatedwith a wound healing protein function. A sample which tests Late (or theequivalent) on the first level classifier is then classified by thesecond level classifier. If one has a big enough set, it is possible toiterate this process further and define a third level classifier on aset of peaks associated with a third protein function and use that for agroup classified by the second level classifiers, etc. As there areoften multiple protein functional groups associated with a given peakthis approach attempts to disentangle compound effects.

Thus, in one aspect of this disclosure a method of generating aclassifier is described including the steps of: a) obtaining adevelopment set of samples from a population of subjects; b) conductingmass spectrometry on the development set of samples and identifying massspectral features present the mass spectra of the development set ofsamples; c) obtaining protein expression data from a large panel ofproteins spanning biological functions of interest for each of thesamples in the development set of samples, or, alternatively, for eachof the samples in an additional cohort of samples with associated massspectral data; d) identifying statistically significant associations ofone or more of the mass spectral features with sets of proteins groupedby their biological function using the set of samples with matched massspectral and protein expression data; and e) with the aid of a computer,using the one or more mass spectral features identified in step d) andclinical data from the development set of samples to train a classifier,the classifier in the form of a set of parameters which assigns a classlabel to a sample of the same type as the development set of samples inaccordance with programmed instructions.

In another aspect, the invention can take the form of a programmedcomputer configured as a classifier generated in accordance with themethod of the previous paragraph.

In another aspect, a method of testing a sample is disclosed, whichincludes the steps of a) training a classifier using a set of massspectra features that have been determined to have statisticallysignificant associations with sets of proteins grouped by theirbiological function; b) storing the parameters of the classifierincluding a feature table of the set of mass spectral features in amemory; c) conducting mass spectrometry on a test sample; and d)classifying the test sample with the trained classifier with the aid ofthe computer. In one variation, the steps include training twoclassifiers using different subsets of features associated withdifferent functional groups of proteins, storing the parameters of thefirst and second classifiers including a feature table of the sets ofmass spectral features in a memory, and logical instructions forcombining the first and second classifiers into a final classifier; c)conducting mass spectrometry on a test sample; and d) classifying thetest sample with the final classifier with the aid of the computer.

In another aspect, a computer configured as a classifier is disclosedincluding a memory storing a feature table in the form of intensity datafor a set of mass spectral features obtained from a biological sample,wherein the set of mass spectra features have been determined to havestatistically significant associations with sets of proteins grouped bytheir biological function, and a set of parameters defining a classifierincluding a classification algorithm operating on mass spectral datafrom a test sample and the feature table.

In another aspect, a classifier development system is disclosed,including a mass spectrometer for conducting mass spectrometry on adevelopment set of samples to generate mass spectral data, said dataincluding a multitude of mass spectral features; a platform forconducting a gene set enrichment analysis on the development set ofsamples or, more typically, another set of samples with associated massspectral data, and identifying statistically significant associations ofone or more of the mass spectral features with sets of proteins groupedby their biological function; and a computer programmed to train aclassifier using the one or more mass spectral features identified bythe platform, the classifier in the form of a set of parameters whichassigns a class label to a sample of the same type as the developmentset of samples in accordance with programmed instructions.

In the above methods and systems, the development set of samples cantake the form of blood-based samples (serum or plasma) from humans, forexample humans enrolled in a clinical trial of a drug or combination ofdrugs. Such humans can be cancer patients. We describe the inventivemethods and systems below in the context of a development set ofblood-based samples obtained from melanoma patients treated with animmunotherapy drug, namely a programmed cell death 1 (PD-1) checkpointinhibitor.

This document demonstrates that it is possible to associate features inmass spectra with biological functions without direct identification ofthe proteins or peptides producing the mass spectral feature, andincorporate biological insights into the choice of mass spectralfeatures for use in reliable classifier training or development, e.g.,using the Diagnostic Cortex platform.

Association of mass spectral features directly with biological processesis important as it is often difficult and time-consuming, and sometimesimpossible, to identify the proteins or peptides producing individualpeaks in mass spectra. This method circumvents the need for thousands ofprotein identification studies, which even when successful, do notalways allow the matching of biological processes to mass spectral peaks(the specific functions of many peptides and protein fragments remain tobe determined).

The ability to find mass spectral features generated from human serumreliably associated with biological processes provides a new way tomonitor these processes in a longitudinal manner in aminimally-invasive, high throughput manner. Serum samples could becollected from patients at many time points during the course of atherapy or disease and changes in specific biological processes couldpotentially be inferred from the analysis of mass spectra generated fromthe serum samples. Such changes can be due to an intervention (e.g.,treatment), or to the natural evolution of the disease. While the studyconsidered an application in oncology, this could be of interest acrossmany disease areas.

The incorporation of biological insights into classifier training forreliable molecular diagnostic test development provides another avenuefor the design and tuning of the tests that can be created. Experiencewith the Diagnostic Cortex platform has shown that in some situationsthe ability to tune tests to meet clinical needs is reduced and similartests are produced despite attempts to tune towards other performancegoals. It was believed that this was due to the dominance of certainmass spectral features and correlations between features, but previousattempts to remove these dominating effects to allow investigation ofother subsidiary, but potentially important, effects had proved quiteunsuccessful. The ability to determine which features are associatedwith individual biological processes provides a new way to look at theuniverse of mass spectral features that can be used in classification,allowing us to attempt to separate out effects that might confound eachother or to remove processes that dominate classification to revealother processes that can improve test performance and test biologicalhypotheses.

The application of GSEA-based feature selection could be very broad andpotentially could lead to the extension of our understanding of the roleof specific biological processes and related treatment in any disease.As an example, type 2 diabetes is known to be a metabolic disorder.However, it is also known that inflammation plays an important role (seeG L King et al., The role of inflammatory cytokines in diabetes and itscomplications. J Periodontol. 2008 August; 79(8 Suppl):1527-34. doi:10.1902/jop.2008.080246 and A O Odegaard et al., Oxidative stress,inflammation, endothelial dysfunction and incidence of type 2diabetes.Cardiovasc Diabetol. 2016 Mar. 24; 15(1):51. doi:10.1186/s12933-016-0369-6). If we decide to build a prognostic test fordiabetes, we might consider selecting separate feature sets: oneassociated with the insulin pathway, and another one with inflammation.Using the approach outlined in this report, we can attempt to separateout the effects of these two broad biological processes on prognosis,and even estimate the relative effect of each of them on the prognosticclassification. Furthermore, if we try to find a predictive test for anovel drug, we might even be able to better understand the mechanism ofaction of the therapy, for example, if only one of the hypotheticallyrelevant feature sets would work well.

Since almost no disease is defined just by a single process disruption,the application of the methods of this disclosure is very broad, and islimited only by the adequate measurement of the related proteins in thesample of choice and by (in)sufficient understanding of the roles ofthese proteins in particular biological processes. So, theoretically,this method could allow researchers to separate and test effects ofmultiple biological processes associated with practically any disorder,as well as to better understand the mechanism of action of treatments.While this study involves MALDI mass spectrometry of serum, and so islimited to processes that can be explored via circulatory proteins andpeptides, the method per se does not depend on sample type and so can beextended even beyond this already wide regime of applicability.

Further details and an example of GSEA-based feature selection with anapplication in classifier development will now be described withparticularity.

Two sample sets were used in this study:

-   -   1. A set of 49 serum samples (“the GSEA cohort”, or “analysis”        set or cohort) from 45 patients with non-small cell lung cancer        and 4 subjects without cancer, for which mass spectral data were        collected and protein expression data were generated using the        1129 protein SOMAscan® aptamer panel (SomaLogic, Boulder,        Colo.). We used this set to determine peaks which were used for        classifier training from a GSEA analysis.    -   2. A set of 119 pretreatment serum samples from 119 patients        with advanced melanoma who were treated with the anti-programmed        cell death—1 (PD-1) therapy, nivolumab, with or without the        addition of a multi-peptide vaccine as part of a clinical trial        (“the NCD cohort”, also called “Moffitt” in this disclosure)        (Details of the trial can be found in J. Weber et al., Safety,        Efficacy, and biomarkers with Vaccine in Ipilimumab-Refractory        or -Naïve Melanoma, J Clin Oncol 2013 Dec. 1; 31(34) 4311).        Outcome data were available for patients in this cohort and mass        spectral data were collected from these samples. Samples from        this set were used in classifier training. This sample set is        described in Example 1.

Generation of Protein Expression Data and GSEA Platform

Protein expression data were collected from the GSEA cohort using the1129 protein SOMAscan aptamer panel by SomaLogic at their laboratory inBoulder, Colo. A list of the 1129 proteins contained in the assay iscontained in Appendix A of our prior provisional application Ser. No.62/340,727 filed May 24, 2016. Further details on the identification ofprotein groups associated with mass spectral features are set forthlater on in this document.

The generation and processing of mass spectral data from both the GSEAcohort and the NCD cohort was performed as explained in great detailpreviously in Example 1.

Application of Gene Set Enrichment Analysis (GSEA) Methods

GSEA (see the Mootha et al. and Subramanian et al. papers citedpreviously) was introduced as a method to help deal with or try tominimize some essential problems in gene expression analysis studies:identification of gene sets and resulting tests in development samplesets that cannot generalize to other sample sets (overfitting), themultiple testing problem, and the inability to identify smallerexpression changes consistent across multiple related genes that mightbe swamped by larger randomly occurring expression changes in a dataset.These are problems inherent in dealing with “p>n” datasets, i.e. wherethe number of measured expression values greatly exceeds the number ofsamples for which the measurements are available. Instead of looking atexpression differences feature by feature (gene by gene), the methodlooks for expression differences that are consistent acrosspre-specified groups or sets of features. The feature sets can becreated based on biological insight or one can use feature sets thathave been defined by prior hypothesis-free studies. Correlating withsets of features rather than single features provides some protectionagainst identifying isolated features that are randomly correlated withstudy groups and would not generalize to other sample sets. Typically,the number of feature sets that are tested for correlation is smallerthan the number of single features in a typical gene expression study,so this reduces somewhat the multiple testing problem. In addition,because the method looks for consistent correlations across a group offeatures, it is possible to identify a significant effect that issmaller in magnitude (per feature) than that which could be identifiedfor a single feature.

Definition of Protein Sets

Specific protein sets were created based on the intersection of the listof SOMAscan 1129 panel proteins and results of queries fromGeneOntology/AmiGO2 and UniProt databases.

The AmiGO2 Queries were Filtered by:

-   -   document category: annotation    -   taxon: H. sapiens    -   evidence type: experimental        The individual filters used are listed in Table 45.

TABLE 45 Filters used in the AmiGO2 database Protein Set Name KeywordAmigo 1 Acute inflammatory response Amigo 2 Activation of innate immuneresponse Amigo 3 Regulation of adaptive immune response Amigo 4 Positiveregulation of glycolytic process Amigo 5 Immune T-cells Amigo 6 ImmuneB-cells Amigo 7 Cell cycle regulation Amigo 8 Natural killer regulationAmigo 9 Complement system Amigo 11 Acute response Amigo 14 Cytokineactivity Amigo 16 Wound healing Amigo 17 Interferon Amigo 18Interleukin-10 Amigo 20 Growth factor receptor signaling Amigo 21 ImmuneResponse Amigo 22 Immune Response Type 1 Amigo 23 Immune Response Type 2The UniProt queries were filtered by:Organism: H. sapiensDB: reviewed (SwissProt)The individual filters used in the UniProt database are listed in table46.

TABLE 46 Filters used in the UniProt database Uniprot 1 Acute phaseUniprot 2 Hypoxia Uniprot 4 CancerThe proteins included in each protein set are listed in the Appendix Dof our prior provisional application Ser. No. 62/340,727. (Uniprot 4Cancer is not included in the listing as it contains more than 400proteins.)

Implementation of GSEA Method

The implementation of the GSEA method was done on a general purposecomputer using Matlab (version R2015a). The process can be decomposedinto several steps.

Let us assume that we have data from a set of N_(s) samples and for eachsample, i, we are given a continuous variable Q_(i) and the expressionvalues of N_(f) proteins, F_(i) ^(j), where i runs over the samples(1≦i≦N_(s)) and j runs over the proteins (1≦j≦N_(f)). We have kpredefined protein sets S_(l) (1≦1≦k) that we are interested incorrelating with the mass spectral data. Each protein set S_(l) consistsof N_(h) ^(l) members (1<N_(h) ^(l)<N_(f)) and is a subset of thecomplete set of N_(f) proteins.

1. Evaluation of the Correlation Between Individual Proteins and aContinuous Variable (the Mass Spectral Feature Value)

The strength of the correlation, r_(j), between each individual proteinj in the full protein set and the continuous variable associated withthe samples is calculated. Spearman's rank correlation was used toassess the degree of correlation. Once a correlation, r_(j), had beencalculated for each protein, j, the N_(f) proteins were ranked by r_(j),from largest to smallest.

2. Calculation of an Enrichment Score

As explained in the Subramanian et al. paper cited previously, theenrichment score, ES_(l), is designed to reflect the degree to whichelements of a particular protein set, S_(l), are over-represented at thetop or bottom of the ranked list of proteins. We start at the top of therank list and construct a running sum, RS(S_(l),p), at item p on theranked list by starting at zero and adding a term |r_(j)|/N_(norm) forthe jth item in the ranked list if protein j is contained in S_(l) andsubtracting a term 1/(N−N_(h) ^(l)) for the jth item in the ranked listif protein j is not contained in S_(l) until one reaches item p.N_(norm) is a normalization coefficient defined by N_(norm)=Σr_(j)|,where the sum runs over all proteins j contained in S_(l). An example ofa calculated RS(S_(l),p) is shown in FIG. 43.

We consider two possible definitions for ES. First, ES_(l) is defined interms of the largest positive value of RS(S_(l),p) as a function of p,RS_(max), and the smallest value of RS(S_(l),p), RS_(min). These areillustrated in FIG. 43. If RS_(max)≧|RS_(min)|, ES=RS_(max); ifRS_(max)<|RS_(min)|, ES=RS_(min) (This is the definition used inSubramanian et al. with their exponent p set to 1.) To be able to takeaccount of protein sets containing mixtures of over- and under-expressedproteins by group or mixture or proteins meaningfully correlated andanti-correlated with the continuous variable, we also consider analternative definition of ES as RS_(max)+|RS_(min)|. (If all proteins inthe protein set are over-expressed (positively correlated), the twodefinitions are identical.)

3. Calculation of the Corresponding p Value

To assess the significance of the deviation of the calculated ES fromits average value for a random distribution, the null distribution of ESis calculated by generating many realizations of a random associationbetween the continuous variable and the protein expressions andevaluating ES for each. These realizations are created by permuting thevalues of the continuous variable assigned to each sample. Note thatthis maintains the correlation structure within the protein expressionvalues for each sample. Once the null distribution has been generated,the p value for the calculated ES can be read off as the proportion ofrandom permutations generating an ES further from random (more extreme)than the calculated ES. (Note that the first definition of ES requiresan assessment of positive and negative ES separately). Examples of thenull and calculated ES for one protein set are shown in FIGS. 44A and44B. The null distribution has to be evaluated separately for eachcomparison (each individual continuous variable (i.e., each massspectral feature) and protein set pair). For the correlation with massspectral feature value, 2000 realizations were generated.

4. Corrections for Multiple Testing

The p values produced by the method outlined above do not take intoaccount multiple testing. It is possible to extend the analysis to takeaccount of multiple testing either by a very conservative Bonferronicorrection or by generating many permutations of the random permutationsover the continuous variable also over the ranked protein list for allprotein sets and computing the ES for each realization. This lattermethod also requires a normalization of the ES to allow the combinationof results across different protein sets. At present neither of theseapproaches has been implemented and the results in this report have notbeen corrected for multiple testing.

GSEA Results

Using the methodology described above, a GSEA p value was obtained foreach mass spectral feature for each protein functional group. Theresults for the correlation of the protein functional groups with theall 351 defined mass spectral features (Appendix A) for the 49 samplesof the GSEA cohort are shown in the heat maps of FIGS. 45A and 45B. Inparticular, FIGS. 45A and 45B show the p values generated by the GSEAanalysis associating all 351 defined mass spectral features withdifferent protein functional groups (biological processes). FIG. 45(a)shows the p values for ES definition1 and FIG. 45(b) for ES definition2. Note: Mass spectral features are ordered in increasing m/z and onlyevery 5^(th) spectral feature is labeled on the x axis.

Table 47 shows the number of mass spectral features (out of the 351defined) associated with the protein functional groups with p valuesbelow a variety of thresholds for each of the protein functional subsetsfor definition 1 and definition 2 of the enrichment score.

TABLE 47 Number of mass spectral features associated with proteinfunctional groups for p values below a variety of thresholds ESDefinition 1 p value ES Definition 2 p value Protein Set Function<0.001* <0.01 <0.03 <0.05 <0.1 <0.0005* <0.01 <0.03 <0.05 <0.1 AcuteInflammation 1 33 65 93 129 5 28 57 79 112 Innate Immune 0 3 6 12 29 0 05 10 27 Response Adaptive Immune 0 1 1 2 5 0 1 4 10 12 ResponseGlycolytic Process 3 3 8 17 36 1 3 9 17 35 Immune T-cells 0 0 3 8 13 0 02 4 4 Immune B-cells 0 2 2 8 15 0 0 2 2 7 Cell cycle 0 1 4 6 10 1 3 8 1520 NK Regulation 0 0 0 2 4 0 0 1 2 7 Complement 15 62 95 122 157 8 44 7793 125 Acute Response 0 9 22 33 64 0 8 30 47 85 Cytokine Activity 0 1 512 18 0 0 2 2 13 Wound Healing 2 15 32 46 78 1 16 40 60 77 Interferon 01 4 6 17 0 5 8 14 20 Interleukin-10 0 2 5 10 25 0 1 7 13 25 GrowthFactor 0 0 2 6 18 0 1 2 4 13 Receptor Signaling Immune Response 1 8 3155 80 1 6 23 46 81 Immune Response 1 2 5 9 16 2 2 3 14 23 Type 1 ImmuneResponse 1 5 10 16 37 0 7 12 18 34 Type 2 Acute phase 8 41 67 84 111 666 90 110 149 Hypoxia 0 0 2 10 26 0 3 14 24 40 Cancer 0 3 11 17 32 1 1830 34 46 *Indicates that ES was greater (or smaller) than that obtainedin any of the realizations generated to assess the null distribution

It is clear that many of the 351 mass spectral features are associatedwith acute phase reactants (acute response, acute phase, acuteinflammation), the complement system, or wound healing. However, therealso exist mass spectral features that are associated with other quitedistinct protein functional groups, such as glycolytic process, cellcycle, or cancer. Hence, it is potentially possible to use measurementsof mass spectral features that have been determined to be associatedwith a particular biological function from serum samples from a patientin order to monitor the particular biological function in the patient.

A cutoff of p=0.05 was chosen for the first definition of enrichmentscore (ES definition 1) and for each protein functional subset, so thatthe mass spectral features with GSEA p<0.05 were taken to be associatedwith the biological function. The mass spectral features associated withseveral of the protein functional subsets (Acute Response, WoundHealing, Immune Response) investigated are tabulated in Appendix E ofour prior provisional application 62/340,727.

We then proceeded to develop classifiers using the FIGS. 8A and 8Bmethodology and peaks associated with particular protein functionalgroups. First, classifier development using the procedure of FIG. 8,steps 102-150 was performed on all 119 samples in the new classifierdevelopment (NCD) cohort, with the subset of 33 mass spectral featuresassociated with the acute response protein functional group. Using theprocedure of FIG. 8, we created a classifier, referred to below as“Classifier 1” which was able to stratify melanoma/nivolumab patientsinto two groups with better and worse prognosis in terms of OS and TTP(Classifier 1). No feature deselection was used, i.e., all 33 massspectral features associated with the acute response protein functionalgroup were used at each step of refinement of the class labels. Fiftyone samples were assigned to the poor performing group and these weregiven an “Early” classification label.

The particular choice of moniker for the class label generated by theclassifier is not particularly important.

The remaining 68 samples, assigned to the good performing group, wereused as the development set for a second classifier generated inaccordance with FIG. 8 steps 102-150, referred to as “Classifier 2”.This classifier was trained on the subset of 26 mass spectral featureswhich had been identified as being associated with wound healing, butnot associated with acute response or immune response. The secondclassifier again used no feature deselection and stratified patientswell into groups with better or worse TTP. Samples in the good TTP groupwere assigned a “Late” classification and samples in the poor TTP groupwere assigned an “Early” classification.

We then defined a final classifier as a hierarchical combination ofclassifiers 1 and 2. The resulting final classifier (i.e., a combinationof two Diagnostic Cortex classifiers with logical instructions for usein a hierarchical manner) uses a total of 59 features, listed inAppendix D of this document. FIG. 46 illustrates schematically how aclassification is assigned to a test sample by the combination ofClassifier 1, based on mass spectral features associated with acuteresponse, and Classifier 2, based on mass spectral features associatedwith wound healing but not with acute response or immune response. Inparticular, the mass spectrum is obtained for a test sample (“testspectrum” in FIG. 46) and in particular feature values for the 59features of Appendix D are obtained. This data is supplied to Classifier1 and Classifier 1 then produces a label for the sample. If the labelreported is Early (or the equivalent) the sample is assigned the Earlyclassification label. If Classifier 1 does not produce the Early label,the spectral data is supplied to Classifier 2. If Classifier 2 producesthe Early label, then the sample is assigned the Early classificationlabel. If Classifier 2 does not produce the Early label, then the sampleis assigned the Late classification label. The Early and Late labelshave the same clinical meaning as explained in Example 1 previously.

Note that this is an example of the creation of multiple differentclassifiers using different feature subsets related to different proteinfunctional groups and the combination of them, by a rule-based system,to produce an overall classification that combines the informationcontent across various biological functions. In this particular examplewe look at the functional groups becoming relevant on the groups definedby the classifier after taking out, or taking care of the main effects,by using the peaks related to the functional groups used in the firstclassifier, and using newly relevant peaks for building a new or secondclassifier in terms of a hierarchy of biological functions. If one has abig enough set, it is possible to iterate this process further anddefine a third level classifier on a set of peaks associated with athird protein function and use that for a group classified by the secondlevel classifiers, etc. As there are often multiple protein functionalgroups associated with a given peak this approach attempts todisentangle compound effects.

Results

1. Classifier 1 Alone

Classifier 1 assigned 51 of 119 samples an “Early” classification. FIGS.47A and 47B show the Kaplan-Meier plots of OS and TTP for theclassifications provided by Classifier 1 for the NCD cohort. Theclassifier based on acute response achieves a clear separation betweenthe good and poor prognosis groups.

2. Classifier 2 Alone

Classifier 2 assigned 35 of the 68 samples not classified as “Early” byClassifier 1 an

“Early” classification and the remaining 33 a “Late” classification.Kaplan-Meier plots of OS and TTP for the classifications provided byClassifier 2 of these 68 samples are shown in FIGS. 48A and 48B.

Classifier 2, using features associated with wound healing, but notthose associated with acute response or immune response, furtherstratifies the 68 samples not classified as “Early” by classifier 1.

2. Final Classifier Defined as a Combination of Classifiers 1 and 2

Combining the classifiers in a hierarchical manner as shown in FIG. 46,one obtains a superior binary stratification of the whole set of 119samples. Thirty three (28%) samples were classified as “Late” and 88(72%) as “Early”. This is illustrated in the Kaplan-Meier plots of OSand TTP for the whole NCD cohort of 119 samples by overallclassification in FIGS. 49A and 49B. Associated statisticscharacterizing the clear stratification of the cohort are given in table48.

TABLE 48 Statistics related to the Kaplan-Meier plots of FIGS. 49A and49B (CPH = Cox Proportional Hazard) OS TTP log-rank p CPH p HR (95% CI)log-rank p CPH p HR (95% CI) Late 0.006 0.008 0.42 <0.001 0.001 0.40 vs(0.22-0.80) (0.23-0.68) Early Median (95% CI) in weeks Median (95% CI)in days Early 80 (59-99) 92 (82-162) Late Not reached (78-undefined) 541(163-undefined)

Table 49 shows some landmark survival and progression-free statisticsand table 50 summarizes the response data.

TABLE 49 Proportions still alive and progression- free at key timepoints Early Late % alive at 1 year 63 88 % alive at 2 years 34 61 %progression-free at 6 months 31 67 % progression-free at 1 year 25 60

TABLE 50 Response by test classification Early (n = 86) Late (n = 33) PR19 (22%) 12 (36%) SD  9 (10%)  9 (27%) PD 58 (67%) 12 (36%)

Table 51 shows the baseline patient characteristic by classificationgroup.

TABLE 51 Baseline patient characteristics by test classification Early(N = 86) Late (N = 33) n (%) n (%) Gender Male   52 (60)   20 (61)Female   32 (37)   13 (39) NA    2 (2)    0 (0) Age Median   62 (16-87)  60 (27-76) (Range) Cohort 1    6 (7)    3 (9) 2   10 (12)    1 (3) 3   6 (7)    5 (15) 4    8 (9)    2 (6) 5   13 (15)    8 (24) 6   43 (50)  14 (42) Prior Ipi No   22 (26)    9 (27) Yes   64 (74)   24 (73) PD-L1expression Positive    6 (7)    2 (6) (5% tumor) Negative   20 (23)    9(27) NA   60 (70)   22 (67) PD-L1 expression Positive   14 (16)    4(12) (1% tumor) Negative   12 (14)    7 (21) NA   60 (70)   22 (67)PD-L1 expression Positive   20 (23)    8 (24) (1% Negative    5 (6)    2(6) tumor/immune NA   61 (71)   23 (70) cells)    Serum LDH <ULN   12(4)    6 (18) levels <2ULN   55 (64)   32 (97) median   496 (174-4914)  472 (149-789) range Baseline tumor median 31.05 (1.50-259.03) 11.43(0.88-1.13) size rangeFisher's exact test shows a significant correlation of serum LDH level<2ULN with classification (p<0.001) and Mann-Whitney p=0.070 forassociation of LDH level with classification. Baseline tumor size wasgreater in the Early group than in the Late group (Mann-Whitneyp<0.001). Classification was not associated with PD-L1 expression at anyavailable cutoff, however.

Multivariate analysis of the time-to-event outcomes allows theadjustment of the effect sizes (hazard ratios) for other knownprognostic characteristics, such as serum LDH level. The results of thisanalysis are given in table 52. Classification remains a significantpredictor of both OS and TTP, in addition to serum LDH level, indicatingthat the classification is providing supplementary information onoutcome following nivolumab therapy.

TABLE 52 Multivariate Analysis of Time-to-Event Endpoints OS TTP CPH pHR (95% CI) CPH p HR (95% CI) Late vs Early 0.023 0.46 (0.23-0.90) 0.0020.43 (0.25-0.74) Female vs Male 0.079 1.63 (0.95-2.82) 0.032 1.65(1.04-2.61) LDH/1000 0.004 1.66 (1.18-2.33) 0.015 1.49 (1.08-2.06) PD-L15% + vs − 0.696 0.81 (0.29-2.30) 0.750 1.14 (0.52-2.50) PD-L1 5% NA0.569 1.19 (0.65-2.20) 0.864 0.95 (0.54-1.67) vs − Prior Ipilimumab0.383 0.77 (0.44-1.37) 0.369 0.80 (0.48-1.31) Yes vs No

It is interesting to note that the performance of the overallclassification obtained using these feature subsets selected usingbiological hypotheses may be superior to that obtained previously onthis sample cohort. FIGS. 50A-50D compare the Kaplan-Meier plots for thepresent classification with those obtained for two previously developedclassifiers, one (IS2=full-set classifier of Example 1) developed usingall mass spectral features simultaneously and the other (IS6) using anensemble of classifiers with clinically different development subsetsagain using all mass spectral features. (These classifiers are describedin Example 1, and Example 8, respectively).

Samples from a cohort of 30 patients also treated with anti-PD 1 therapywere available for independent validation of the classifier. Twenty-onepatients (70%) were classified as “Early” and 9 (30%) as “Late”. TheKaplan-Meier plot for OS is shown in FIG. 51 and associated statisticsin table 53. (TTP was not available for this observational cohort.)

TABLE 53 Statistics related to the Kaplan-Meier plots of FIG. 51 OSlog-rank p CPH p HR (95% CI) Late vs 0.016 0.030 0.20 (0.05-0.86) EarlyMedian (95% CI) in weeks 1 year survival 2 year survival Early  37(21-68) 38% 27% Late 210 (20-undefined) 89% 89%

Example 6 Conclusions and Discussion

This study of Example 6 has demonstrated that it is possible to:

1) associate features in mass spectra with biological functions withoutdirect identification of the proteins or peptides producing the massspectral feature, and2) incorporate biological insights into the choice of mass spectralfeatures for use in reliable classifier development, e.g., using theDiagnostic Cortex platform of FIG. 8.

It will be further appreciated that once a classifier has been developedas explained above, it is then stored as a set parameters in memory of acomputer (e.g., feature table of mass spectral features used forclassifications, identification of mini-classifiers, logistic regressionweights, kNN parameters, program code for executing one or more masterclassifiers and logic defining a final classifier, as per FIG. 8 step150 or FIG. 46, etc.). A laboratory test center, for example asdescribed in FIG. 15, includes such a computer as well as a massspectrometer to conduct mass spectrometry on a blood-based sample. Theresulting mass spectrum is subject to pre-processing steps (same asperformed on the samples of the classifier development set) and then theclassifier is applied to the mass spectral data of the sample. Theclassifier then generates a class label, e.g., Early or Late, andprovides the class label to a requesting physician or clinic as a feefor service.

With reference to FIG. 52, it will be further appreciated that aclassifier development system 5200 has been disclosed which includes amass spectrometer 5202 for conducting mass spectrometry on a developmentset of samples, or, alternatively and more typically, anotherindependent set of samples, to generate mass spectral data. The dataincludes intensity data for a multitude of mass spectral features. Thesystem includes a platform 5204 for conducting a gene set enrichmentanalysis on the development set of samples, or, more typically, theother independent sample set, including a protein assay system such asthe SOMAscan system of SomaLogic or the equivalent, and a computer foridentifying statistically significant associations of one or more of themass spectral features with sets of proteins grouped by their biologicalfunction. The system further includes a computer 5206 programmed totrain a classifier on the development set of samples using the one ormore mass spectral features identified by the GSEA platform, e.g., usingthe procedure of FIG. 8. The classifier is in the form of a set ofparameters and programmed instructions which assign a class label to asample of the same type as the development set of samples in accordancewith the programmed instructions.

In this document we use the terms classifier training and classifierdevelopment interchangeably, to mean a process of constructing aclassifier in a computer (i.e., specifying the parameters for such aclassifier) and testing its ability to classify a set of samples (thedevelopment set of samples or some subset thereof). Typically, thisprocess occurs in an iterative manner to tweak the parameters tooptimize classifier performance, such as by refining class labelsassigned to members of the development set, refining filteringparameters, feature deselection, varying the parameter k, etc. It willalso be noted that while the present example describes the use ofk-nearest neighbor with majority vote as a classification algorithm, inprinciple the invention can use other supervised learning classificationalgorithms, such as margin-based classifiers, support vector machine,decision trees, etc., or a classifier configured as a multitude offiltered mini-classifiers combined using a regularization procedure, forexample as generated using the procedure of FIG. 8.

The following clauses are offered as further descriptions of theinvention disclosed in Example 6.

1. A classifier for use in guiding immune checkpoint inhibitor treatmentfor a cancer patient, comprising:

a memory storing a reference set of class-labelled mass spectral dataobtained from blood-based samples of melanoma patients treated with animmune checkpoint inhibitor agent, the mass spectral data in the form offeature values for at least 50 mass spectral features, wherein the massspectral features are identified with proteins circulating in serumassociated with at least the following biological processes: (1) acutephase, (2) acute response, (3) complement system, and (4) acuteinflammatory response; and

a programmed computer implementing a classification algorithm on a setof mass spectral data including feature values for the multitude of massspectral features obtained from a test blood-based sample and thereference set and generating a class label for the test blood-basedsample.

2. The classifier of clause 1, wherein the mass spectral featuresinclude the features listed in one of Appendix A, Appendix B, AppendixC, or Appendix D.

3. The classifier of clause 1, wherein the mass spectral featuresfurther include features associated with the following additionalbiological processes: immune response type 2 and interleukin-10.

4. The classifier of clause 1, wherein the mass spectral data of thetest blood-based sample and the reference set samples is acquired fromat least 100,000 laser shots performed on the samples using MALDI-TOFmass spectrometry.

5. The classifier of clause 1, wherein the test blood-based sample isobtained from a melanoma patient. 6. The classifier of clause 1, whereinthe immunotherapy comprises an antibody drug targeting programmed celldeath 1 (PD-1).

7. The classifier of clause 1, wherein the immunotherapy comprises anantibody drug targeting CLTA4.

8. A method of training a classifier, comprising the steps of:

a) obtaining a development set of samples from a population of subjectsand optionally a second independent set of samples from a similar, butnot necessarily identical population of subjects;

b) conducting mass spectrometry on the development set of samples, andoptionally on the second set of samples, and identifying mass spectralfeatures present in the mass spectra of the set(s) of samples;

c) obtaining protein expression data from a large panel of proteinsspanning biological functions of interest for each of the samples in thedevelopment set of samples or each of the samples in the second set ofsamples;

d) identifying statistically significant associations of one or more ofthe mass spectral features with sets of proteins grouped by theirbiological function; and

e) with the aid of a computer, training a classifier on the developmentset of samples using the one or more mass spectral features identifiedin step d), the classifier in the form of a set of parameters whichassigns a class label to a sample of the same type as the developmentset of samples in accordance with programmed instructions.

9. The method of clause 8, wherein step d) further comprises the step ofperforming a gene set enrichment analysis.

10. The method of clause 8 or clause 9, wherein the classifier is in theform of a filtered combination of mini-classifiers which have beensubject to a regularization procedure.

11. The method of any one of clauses 8-10, wherein the samples in thedevelopment set, and optional second sample set, are blood-basedsamples.

12. The method of any one of clauses 8-11, wherein step b) comprisessubjecting each of the samples in the sample set(s) to at least 100,000laser shots in MALDI-TOF mass spectrometry.

13. The method of clause 8, wherein the classifier trained in step e) isdeemed a first classifier, and the method further comprising repeatingstep e) for a second set of one or more mass spectral featuresassociated with a different group of proteins associated with adifferent biological function, thereby training a second classifier.

14. The method of clause 13, further comprising the step of defining afinal classifier from a combination of the first and second classifiers.

15. The method of clause 13, wherein the second classifier is used tofurther stratify members of a classification group assigned by the firstclassifier.

16. A computer configured as a classifier trained in accordance with anyof clauses 8-15.

17. A method of testing a sample, comprising steps of:

a) training a classifier in accordance with any of clauses 8-11;

b) storing the parameters of the classifier including a feature table ofthe set of mass spectral features in a memory;

c) conducting mass spectrometry on a test sample; and

d) classifying the test sample with the trained classifier with the aidof the computer. 18. A method of testing a sample, comprising the stepsof:

a) training a first classifier and a second classifier in accordancewith clause 13;

b) storing the parameters of the first and second classifiers includinga feature table of the sets of mass spectral features in a memory, andlogical instructions for combining the first and second classifiers intoa final classifier;

c) conducting mass spectrometry on a test sample; and

d) classifying the test sample with the final classifier defined in stepb) with the aid of the computer.

19. A computer configured as a classifier comprising:

a memory storing a feature table in the form of intensity data for a setof mass spectral features obtained from a development set of biologicalsamples, wherein the set of mass spectra features have been determinedto have statistically significant associations with sets of proteinsgrouped by their biological function present in the biological sample;

a set of parameters defining a classifier including a classificationalgorithm operating on mass spectral data from a test sample and thefeature table.

20. A classifier development system, comprising:

a mass spectrometer for conducting mass spectrometry on a developmentset of samples, and optionally a second independent set of samples, togenerate mass spectral data, said data including a multitude of massspectral features;

a platform for conducting a gene set enrichment analysis on thedevelopment set of samples, or optionally the second independent set ofsamples, and identifying statistically significant associations of oneor more of the mass spectral features with sets of proteins grouped bytheir biological function; and

a computer programmed to train a classifier on the development set ofsamples using the one or more mass spectral features identified by theplatform, the classifier in the form of a set of parameters whichassigns a class label to a sample of the same type as the developmentset of samples in accordance with programmed instructions.

21. The system of clause 20, wherein the development set of samples, andoptional second independent set of samples, are blood-based samples fromhumans.

22. The system of clause 21, wherein the blood-based samples for thedevelopment sample set are obtained from melanoma patients treated withan immunotherapy drug.

23. A classifier training method, comprising the steps of:

a) obtaining a development set of samples, and optionally a secondindependent sample set, from a population of subjects;

b) conducting mass spectrometry on the development set of samples andoptional second set of samples, and identifying mass spectral featurespresent in the mass spectra of the sets of samples;

c) obtaining protein expression data from a large panel of proteinsspanning biological functions of interest for each of the samples in thedevelopment set of samples or each of the samples in the optional secondindependent sample set;

d) identifying statistically significant associations of one or more ofthe mass spectral features with sets of proteins grouped by theirbiological function;

e) with the aid of a computer, training a first classifier on samplesfrom the development sample set using the one or more mass spectralfeatures identified in step d), the classifier in the form of a set ofparameters which assigns a class label to a sample of the same type asthe development set of samples in accordance with programmedinstructions, the classifier generating at least a first class label anda second class label, and

f) with the aid of the computer, training a second classifier using adifferent set of one or more mass spectral features identified in stepd) associated with a different set of proteins grouped by a differentbiological function, and

g) classifying a sample with the first classifier wherein if the firstclassifier generates the first class label reporting the class label andif the first classifier generates the second class label using thesecond classifier to further stratify the sample.

24. A classifier training method comprising the steps of:

(a) performing both mass spectrometry and gene set enrichment analysison a development set of blood-based samples or alternatively performingmass spectrometry on a development set of blood-based samples and asecond independent set of blood-based samples and gene set enrichmentanalysis on the second set of samples;

(b) identifying a plurality of sets of mass spectral peaks which havestatistically significant associations with sets of proteins grouped bytheir biological function;

(c) executing in a computer a classifier training procedure using one ofthe sets of peaks identified in step b) associated with a first proteinfunctional group, the classifier training procedure classifying the massspectral data of the development set of samples or a subset thereof.

25. The method of clause 24, further comprising repeating step c) for asecond set of peaks identified in step b) associated with a secondprotein functional group different from the first protein functionalgroup.

26. The method of clause 25, further comprising repeating step c) for athird set of peaks identified in step b) for a third protein functionalgroup different from the first and second protein functional groups.

27. A method of testing a subject, comprising:

training a classifier in accordance with clause 24;

classifying a sample from the subject at a first point in time with theclassifier;

classifying a second sample obtained from the subject at a later pointin time with the classifier.

28. The method of clause 27, wherein the sample is provided by a patientenrolled in a clinical trial of a drug, wherein the first point in timeis in advance of treatment by the drug, and wherein the later point oftime is after treatment is commenced and the patient is still enrolledin the clinical trial.

29. A method of evaluation of a biological process within a human,comprising the steps of:

a) training a classifier in accordance with clause 24;

b) conducting mass spectrometry on a blood-based sample from the human;

c) classifying the sample using data obtained from step b) and theclassifier trained in step a) and thereby obtaining a first class labelfor the sample;

d) conducting mass spectrometry on a second blood-based sample from thehuman taken at a later point in time from the time the sample of step b)was obtained;

e) classifying the second blood-based sample using data obtained fromstep 3) and the classifier trained in step a) and thereby obtaining asecond class label;

f) comparing the first and second class labels, wherein the comparisonprovides information regarding a biological process occurring within thehuman.

30. A method of evaluation of a biological process within a human,comprising the steps of:

a) obtaining a development set of blood-based samples from a populationof subjects and optionally a second independent set of blood-basedsamples from a similar, but not necessarily identical population ofsubjects;

b) conducting mass spectrometry on the development set of blood-basedsamples, and optionally on the second set of blood-based samples, andidentifying mass spectral features present in the mass spectra of theset(s) of blood-based samples;

c) obtaining protein expression data from a large panel of proteinsspanning biological functions of interest for each of the blood-basedsamples in the development set of samples or each of the samples in thesecond set of blood-based samples;

d) identifying statistically significant associations of one or more ofthe mass spectral features with sets of proteins grouped by theirbiological function;

e) conducting mass spectrometry on a blood-based sample from the humanincluding obtaining values of features in the mass spectrum of one ormore of the mass spectral features which were identified in step d).

31. The method of clause 30, further comprising the steps of obtaining asecond blood-based sample from the human, and conducting massspectrometry on the second blood-based sample from the human includingobtaining values of features in the mass spectrum of one or more of themass spectral features which were identified in step d).

32. The method of clause 31, wherein the human is enrolled in a clinicaltrial of a drug or combination of drugs.

33. The method of clause 30, wherein the human is enrolled in a clinicaltrial of a drug or combination of drugs, and wherein the method furthercomprises the steps of repeatedly obtaining blood-based samples from thehuman over the course of the human's enrollment in the clinical trial,and conducting mass spectrometry on the blood-based samples includingobtaining values of features in the mass spectrum of one or more of themass spectral features which were identified in step d) as with sets ofproteins grouped by their biological function.

33. A method of monitoring a set of patients enrolled in a clinicaltrial, comprising performing the method of clause 30 on each of patientsenrolled in the clinical trial.

34. The method of clause 33, further comprising repeatedly obtainingblood-based samples from the patients enrolled in the clinical trialover the course of the trial, and conducting mass spectrometry on theblood-based samples including obtaining values of features in the massspectrum of one or more of the mass spectral features which wereidentified in step d) of clause 30 as being associated with sets ofproteins grouped by their biological function.

Example 7 Longitudinal Studies

We conducted an analysis of samples collected during treatment of thenivolumab study (described in Example 1), and specifically at weeks 7(“WK7”) and weeks 13 (“WK13”) of the trial. We explored how theclassifications for a given patient changed over time, using thefull-set classifier of Example 1 and the classifier of Example 2. Wefound that in some patients the labels changed e.g., an initial classlabel of Late at the commencement of treatment, followed by Late at week7 and Early at week 13. As another example, some patients had the classlabel of Early at commencement of treatment, followed by Early at week 7and Late at week 13.

The results for the longitudinal studies using the Example 1 full-setclassifier are shown in FIGS. 30A-30F. FIGS. 30A and 30B areKaplan-Meier plots for overall survival (FIG. 30A) and time toprogression (TTP)(FIG. 30B) for Early and Late groups as defined by thebaseline classifications. FIGS. 30C and 30D are Kaplan-Meier plots foroverall survival and TTP, respectively for Early and Late groups asdefined by the week 7 classifications. FIGS. 30E and 30F areKaplan-Meier plots for overall survival and TTP, respectively for Earlyand Late groups as defined by the week 13 classifications, for the 90patients for which we had class labels at all three time points. Table54 is a table of the survival analysis for the plots of FIG. 30A-30F.

TABLE 54 HR (95% CI) log-rank p value Medians Baseline OS 0.45(0.21-0.78) 0.008 Early: 84 weeks, Late: Not reached WK7 OS 0.39(0.14-0.64) 0.002 Early: 60 weeks, Late: 113 weeks WK13 OS 0.33(0.14-0.54) <0.001 Early: 61 weeks, Late: Not reached Baseline 0.53(0.24-1.01) 0.055 Early: 91 days, Late: 457 days TTP WK7 TTP 0.37(0.10-0.61) 0.003 Early: 91 days, Late: 782 days WK13 TTP 0.58(0.25-1.15) 0.112 Early: 112 days, Late: 457 daysTable 55 shows the distribution of the classifications across the threetime points for all samples.

TABLE 55 Distribution of classifications across the three time points:Baseline, WK7, WK13. Missing classifications are denoted by “-”.Classifications n Early Early 15 Early Early Early - 8 Early - - 8 EarlyEarly 1 Late Early Late 1 Early Early Late 14 Late Late Early 3 EarlyLate Early 3 Late Late Early - 3 Late Late 12 Early Late Late Late 41Late Late - 6 Late - - 4

The majority of classifications remain the same across the availabletime points (82/119=69%). There are proportionately more changes fromEarly to Late than from Late to Early and most of the changes from Earlyto Late occurred at WK7 and remained Late at WK13. It is possible thatthis is due to the onset of the immunotherapy treatment. Half of thepatients with a change from Late to Early at WK7 reverted back to Lateat WK13, when the sample was available. Twelve (16%) of the patientsclassified as Late at WK7 changed to Early at WK13 and half of theseprogressed between 70 and 93 days, although three of the othersexperienced progression-free intervals in excess of 1000 days.

FIGS. 31A and 31B show Kaplan-Meier curves that plot the outcomes whenthe patients are grouped according to their triplet of baseline, WK7,and WK13 classifications. FIG. 31A is a plot of overall survival; FIG.31B is a plot of time to progression. In these figures, the groups arelabeled by the baseline classification first, the WK7 classificationsecond, and the WK13 classification last (i.e. “Early Late Early”indicates baseline classification of Early, WK7 classification of Late,and WK13 classification of Early). Repeated Early classifications markparticularly poor OS and TTP and, at least in OS, having an Early labelat WK 13 indicates poorer prognosis, even when the previous twoclassifications were Late. However, a Late label at WK13 and WK7corresponded to better outcomes, even if the baseline classification hadbeen Early. Note: there were too few patients with other label sequencesfor a meaningful analysis.

Table 56 shows the medians for the plots of FIGS. 31A and 31B.

TABLE 56 Median OS Median TTP (days) (weeks) from day 84 Early EarlyEarly (N = 15) 41  84 Early Late Late (N = 14) 94 789 Late Late Early (N= 12) 78 Not reached Late Late Late (N = 41) Not reached 457

We repeated this analysis for the classifications produced by theExample 2 classifier over time. The results are generally similar tothose presented here for the Example 1 full set classifier. The majorityof classifications remain the same across the available time points(59/90=66% across all three time points, 83/107=78% across the first twotime points). There are proportionately more changes from Early to Latethan from Late to Early and most of the changes from Early to Lateoccurred at WK7 and remained Late at WK13. It is possible that this isdue to the onset of the immunotherapy treatment. Most of the patientswith a change from Late to Early at WK7 reverted back to Late at WK13,when the sample was available.

FIG. 32A and FIG. 32B are Kaplan-Meier plots which show the outcomeswhen the patients are grouped according to their triplet of baseline,WK7, and WK13 classifications produced by the Example 2 classifier. FIG.32A is a plot of overall survival; FIG. 32B is a plot of time toprogression. The groups are labeled by the baseline classificationfirst, the WK7 classification second, and the WK13 classification last.Repeated Early classifications mark particularly poor OS and TTP, whilerepeated Late classifications indicate particularly good OS and TTP.Changing from Early to Late at WK7 and staying Late at WK13 leads tosimilar outcomes to having a Late classification at all three timepoints.

The statistics for FIG. 32A are set forth in table 57.

TABLE 57 Medians for the OS plots of FIG. 32A Median OS (weeks) EarlyEarly Early (N = 9)  47 Early Early Late (N = 5)  99 Early Late Late (N= 11) Not reached Late Early Late (N = 5)  99 Late Late Early (N = 6) 78 Late Late Late (N = 50) 113

The statistics for FIG. 32B are set forth in table 58.

TABLE 58 Medians for the TTP plots of FIG. 32B Median TTP (days) fromday 84 Early Early Early (N = 5)  78 Early Late Late (N = 9) 789 LateLate Late (N = 38) 782

We have some theories for why the class labels changed over time. It ispossible that the class label changes were induced by biological changesin the patients caused by the commencement of the nivolumab therapy. Itis possible that the changes were due to the influence on tumor size onclassification labels (see the discussion below), and large tumorshrinkage that some patients achieve. Whatever the origin of thechanges, we do observe that most patients keep their baseline label. Ofthose patients whose class label changes over time, we observe that whenthe label changes from Early at baseline to Late later on (week 7 or 13)these patients have relatively good outcome, similar to those patientshaving a Late baseline class label. Accordingly, in one embodiment, thetest of Example 1 or 2 can be conducted periodically over the course oftreatment, e.g. every 4, 6 or 8 weeks. By comparing the results and theprogression of class labels over time during treatment it may bepossible to monitor the therapeutic effect of the nivolumab treatment,or predict the patient's prognosis or overall survival. This treatmentmonitoring can be direct, i.e., direct changing of some immune statusmeasured by the class label, or indirect, i.e., the change in classlabel is a proxy or approximation of measurement of tumorshrinkage/expansion. How often one would want to conduct the test anddetermine the patient's class label during the course of treatment mightalso depend on whether the change in class label is due to direct actionof the drug on the patient's immune system or whether one has to waitfor an indirect effect of the treatment on shrinkage (or lack thereof)of the tumor.

A change of the patient's label over time from Late to Early may be anearly indication of lack of efficacy of the drug. This lack of efficacycould be optionally confirmed by conducting radiological studies of thepatient, e.g., CT scan to determine tumor size and change as compared tobaseline. Potentially, if the changes from Late to Early are due to thedrug changing the immune state of the patient directly and if thishappens in a relatively short space of time (say 4 weeks or so) thebaseline Late label could be an indication to commence treatment withnivolumab and the subsequent Early label could be used to either stoptreatment, change treatment to a different treatment (such ascombination nivolumab and ipilimumab) or take other action. Anotherpossibility would be to conduct a monitoring test periodically duringthe first few weeks of treatment and use the class labels to indicatehow long the patient needs to take the nivolumab. Currently, patientstake the drug until disease progression. This can be a long time and thedrug is very expensive. So, if there is a way to tell within the firstfew months whether a patient could stop nivolumab treatment withoutdetriment to outcome it could result in some savings to health carecosts. In any event, in one possible embodiment, the tests of Examples 1and 2 are conducted periodically over the course of treatment. The classlabels are compared over the course of treatment. The status of theclass label over the course of treatment can be used to guide treatmentor predict the patient's prognosis, either maintain the treatment, stopthe treatment, or change the treatment in some fashion such as bycombining nivolumab with another drug in a combination treatment regime.

In one specific example of how the monitoring tests can be done, theinitial class label is determined in accordance with Example 1 orExample 2 using the system of FIG. 15 (described above), at least onceagain within the first four weeks of treatment, and at least once againafter the first four weeks of treatment.

The following clauses are offered as further descriptions of theinventions described in Example 7.

1. Classifying a patient sample initially in accordance with themethodology of Example 1, 2, 3, 4, or 6 and repeatedly over the courseof treatment or over the course of the patient enrollment in a clinicaltrial obtaining a sample from the patient, conducting mass spectrometryof the sample, and classifying the sample with the classifier and thusgenerating a class label repeatedly.

2. The method of clause 1, further comprising the step of determining ifthe class label changes over the course of treatment and using thechange in class label to guide treatment of the patient.

3. The method of clause 1, further comprising the step of determining ifthe class label does not change over the course of treatment and usingthe absence of change in the class label to guide treatment of thepatient.

4. The method of clause 1, wherein the repeatedly conducting step isperformed initially in advance of treatment, at least once again withinthe first four weeks of treatment, and at least once again after thefirst four weeks of treatment.

5. As indicated in Example 6, a method of determining the associationbetween a biological function and mass spectral features obtained from ablood-based sample (e.g., using GSEA), by repeatedly obtainingblood-based samples from a patient and performing mass spectrometry onthe samples, and analyzing the mass spectral data from the samples overthe course of time to observe or understand changes in the biologicalfunction over time, for example up regulation or down regulation ofparticular proteins associated with the biological functions, e.g., overthe course of treatment or over the course of a patient participation ina clinical trial.

6. The invention of clause 5, further including the step of performing aclassification of each of the blood-based samples using acomputer-implemented classifier trained from a development set ofsamples and a set of mass spectrometry features associated with thebiological function.

Example 8 Classifiers Trained from Tumor Size Information

We have discovered a method for generating a classifier that takes intoaccount tumor size at baseline which improves the classifierperformance. This method and how it is used in practice will beexplained in this section. Note that the studies described below usedthe same 119 samples of Example 1, tumor size data was provided for allpatients, and we used the same sample feature table data (mass spectraldata for features in Appendix A) as we did for Example 1. The onlydifference was that feature m/z 9109 was dropped from the feature tableas it has possible reproducibility issues and little value forclassification.

Initial attempts at taking account of tumor size in the assignment of aprognostic label for melanoma patients treated with nivolumab indicatedthat for patients who had available tumor size follow data on treatment,there was a definite influence of tumor size at baseline on theclassification we should assign to the samples in the development set.We noticed this by taking the data of the 104 patients for whom weinitially had baseline and follow up tumor size data and splitting theset into two: one half with smaller tumors at baseline and the otherhalf with larger tumors at baseline. We then used the classifierdevelopment method of FIG. 8 as we had done to make the classifier ofExample 1, and made separate classifiers, one for patients with smallertumors and one for patients with larger tumors. We then proceeded toclassify the samples in the development set using either the large orsmall tumor classifiers, depending in the size of the tumor at baseline.

We noticed that some of the smaller tumors classified by the Example 1full-set classifier as Late now got an Early classification and some ofthe larger tumors that had been classified by the Example 1 full setclassifier as Early were now classified as Late. In particular, some ofthe small tumors that previously were classified as Late and which haddemonstrated huge tumor growth on treatment were now classified as Earlyand some of the large tumors that previously had been classified asEarly and which had shrunk considerably in the first 26 weeks oftreatment were now classified as Late. Plotting the Kaplan-Meier plotsfor this subset of 104 patients, taking the classifications from the twoseparate classifiers, as defined by pretreatment tumor size, increasedthe hazard ratio between the new Early and Late groups, as shown inFIGS. 33A and 33B. In these figures, “Original Early/Late” are theclassification groups defined for the 104 patient subset using Example 1full-set classifier, approach 1, and “TSAdjusted Early/Late” are theclassifications generated by the new classifiers for the smaller andlarger tumors, each applied to the samples with smaller and largertumors, respectively.

The results of the survival analysis comparison between the Early andLate groups adjusted for tumor size are given in table 58.

TABLE 58 Performance of classifications obtained for the subset of 104patients when adjusted for tumor size Median OS Median TTP #Early/ HR OSLog- Earlier/Later HR TTP Log- Earlier/Later #Late (95% CI) rank p(weeks) (95% CI) rank p (days) TS 44/60 0.24 <0.001 69/not 0.36 <0.001 88/541 Adjusted (0.11-0.37) reached (0.18-0.49) Original 36/68 0.420.002 73/not 0.57 0.015 157/362 (0.20-0.68) reached (0.32-0.88)These results indicated to us that we were not making optimal decisionson classification for some of the samples with the smallest and largesttumors, and that we could improve by taking tumor size into account whendesigning and generating the classifier of Example 1.

We then obtained baseline tumor size data for the remaining 15 samples.When we applied the new classifiers to these samples (depending onwhether they were in the small tumor size group or large tumor sizegroup), and added these patients into the Kaplan-Meier analysis, wenoticed that the improved separation almost disappeared. Apparently, wewere doing a worse job of classifying these 15 samples than we had donebefore. We also tried to carry out the same approach as above trainingon all 119 samples, but again the result was more or less no improvementfrom our initial classification. These observations led us to theconclusion that these 15 patients whom we had initially omitted wereessentially different—indeed they were omitted because they did notreach a follow up tumor size assessment, having very early progression(all 15 patients progressed before 78 days). We hypothesized that forpatients who progress very quickly, tumor size plays a much weaker rolethan it does for the patients who remain progression free for a longerperiod of time. To try to keep the improvement noted above for the 104patients reaching the 26 week assessment and classify the other 15samples correctly, we decided to first find a classifier to remove thepatients progressing the fastest, and then repeat classifier developmentby tumor size for the remaining samples. That is, we wanted to removefrom classifier development those samples with the patients progressingfastest, and then conduct a new classifier development taking intoaccount tumor size, and generate a “small tumor” classifier and a “largetumor” classifier. These new classifiers are designed for later use intesting a patient for immune checkpoint inhibitor benefit, with theadditional input at the time of testing data on whether the patient hasa “large” or “small” tumor and then using the appropriate large or smalltumor classifier. It will be apparent from the following discussion thatthe methodology we describe below may be useful generally in generating“small tumor” and “large tumor” classifiers in the oncology setting.

In order to remove the patients progressing the fastest, we returned tothe full set Example 1 classifier. Using this classifier, we divided thesamples in the development set into Early (N=47) and Late (N=72) groups.We took the Early group of 47 samples and used the same methodology ofFIG. 8 as detailed above, using the Early group of 47 samples as theinput development sample set. In performing the new classifierdevelopment we performed label flips for misclassified samples in aniterative manner until convergence to make a classifier that splitsthese 47 samples into two sub-groups, which we called “Earlier” and“Later”. The initial class definitions (FIG. 8, step 102) were based onshorter and longer OS and we used filtering (FIG. 8 step 126) based onhazard ratio for OS between the classification groups of the trainingset. This produced a classifier that split the 47 Early patients intotwo groups with better (“Later”) and worse (“Earlier”) outcomes.

The Kaplan-Meier plots for OS and TTP for the groups generated by thisclassifier are shown in FIGS. 34A and 34B. Twenty two patients wereassigned to the Earlier group and 25 to the Later group. The results ofthe survival analysis comparison between the Earlier and Later groupsare given in table 59.

TABLE 59 Performance of classifier developed on only full-set classifier“Early” samples Median OS Median TTP HR OS Log- Earlier/Later HR TTPLog- Earlier/Later #Earlier/#Later (95% CI) rank p (weeks) (95% CI) rankp (days) 22/25 0.57 0.094 26/73 0.60 0.085 77/132 (0.27-1.10)(0.31-1.07)

It is apparent that the Earlier group has particularly poor outcomes interms of TTP and OS. We decided to remove these 22 samples from furtheranalysis and leave them their already assigned “Early” classification.We then conducted two new classifier developments, again using theprocedure of FIG. 8, one for large tumors and one for small tumors,using the remaining 97 samples of the initial development sample set.This set of 97 samples was split into two groups depending on tumorsize: the smallest 49 samples were used to generate one classifier(small tumor classifier) and the largest 48 samples were used togenerate another classifier (large tumor classifier). Both classifierswere trained as before using the procedure of FIG. 8 in an iterativemanner, with label flips for misclassified samples until convergence,with the initial class assignments of “Early” and “Late” (FIG. 8, step102) defined based on duration of OS.

When the data for the patients with larger tumors, patients with smallertumors and quickly progressing patients (Early/Earlier classification)were combined, the Kaplan-Meier plots of FIGS. 35A and 35B wereobtained. The results of the survival analysis comparison between theEarly and Late groups are given in table 60.

TABLE 60 Performance of classifications obtained for all 119 patientswhen adjusted for tumor size, first removing the 22 patients classifiedas having especially poor outcomes Median OS Median TTP #Early/ HR OSLog- Earlier/Later HR TTP Log- Earlier/Later #Late (95% CI) rank p(weeks) (95% CI) rank p (days) Final 60/59 0.32 <0.001 61/not 0.40<0.001 83/490 (0.19-0.51) reached (0.24-0.57) Original 47/72 0.38 0.00261/not 0.50 0.001 84/230 (0.19-0.55) reached (0.29-0.71)The final Late group has slightly better outcomes than the original Lategroup and is composed of significantly fewer patients. Outcomes in thefinal Early group are quite similar to those of the original Earlygroup, although its size has increased by 28%. The hazard ratios betweenthe groups are slightly better for both endpoints than they were for theoriginal Example 1 classifications.

Investigation of classifier performance in this context can also be madeby plotting the percent change in tumor size from baseline to a laterpoint in time (e.g., 26 weeks after commencement of treatment) for eachmember of the development set, and indicating in such a plot whether thedata points represent Early or Late classified patients. Such plots,known as “waterfall plots,” are shown in FIGS. 36A and 36B. Thewaterfall plots show the percentage reduction in tumor size for the 104patients assessable at the 26 week evaluation. FIG. 36A is the plot forthe “final” classifiers (i.e., taking into account tumor size and usingeither the large or small tumor classifier). FIG. 36B is the plot forthe original Example 1 full-set classifier.

What is noticeable from comparing the plots of FIGS. 36A and 36B is thatthe new final classifications using tumor size classifiers isconsiderably better at classifying the patients with tumor growth asEarly. That is, the majority of patients having significant tumor growthover the course of treatment were classified as Early when the tumorsize classifiers were used, as would be expected given the clinicalmeaning the Early class label. Moreover, the majority of the patientswith significantly diminished tumor size over the course of treatmentwere classified as Late, which is also expected. However, comparing theright hand side of FIGS. 36A and 36B, the new classifiers using tumorsize data performed slightly worse in identifying the patients withtumor shrinkage as Late as compared to the Example 1 full setclassifier.

A preferred method for generating classifiers using tumor size data in adevelopment sample set can be summarized and explained in flow chartform. Referring now to FIG. 37, the classifier development process isshown at 3700 and includes a first step 3702 of removing the samples ofpatients from the development sample set who progressed fastest aftercommencement of treatment. This step 3702 is shown in detail in FIG. 39and will be explained in detail below.

At step 3704, once these samples are removed from the development set,the samples remaining in the development set are sorted based on tumorsize at baseline into small tumor and large tumor groups, 3706 and 3707,respectively.

At step 3708, the small tumor samples (associated mass spectral data,feature values of features listed in Appendix A) are used as thedevelopment set for generating a small tumor classifier using theprocedure of FIG. 8.

At step 3710, the performance of the classifier developed at step 3708is then verified by using the classifier to classify the small tumorsamples 3706.

At step 3712, the parameters of the small tumor classifier generated atstep 3708 are then stored for later use in classifying test samples forpatients with small tumors. These parameters include, inter alia, thedata identifying the small tumor sample mass spectra data sets formingthe reference set for classification; the feature values at predefinedmass spectral features for the reference set; identification and kNNparameters of the mini-classifiers passing filtering; logisticregression weights derived from the combination of mini-classifiers withdrop-out regularization; and the definition of the final classifier fromthe master classifiers generated during the performance of FIG. 8 on thesmall tumor development sample set (FIG. 8B, step 150).

The steps 3714, 3716 and 3718 are performed on the large tumor samples3707, exactly the same as for steps 3708, 3710 and 3712 described above.

The manner of use of the classifiers generated in accordance with FIG.37 is shown in FIG. 38. The classifiers are used for conducting a testof blood-based sample of a melanoma or other cancer patient to determinewhether they are likely or not to obtain benefit from an immunecheckpoint inhibitor in treatment of cancer, such as anti-PD-1 antibodyor anti-CTLA4 antibody. This process is shown at 3800. At step 3802,tumor size data for the patient is obtained. Such data could be obtainedfrom CT or PET scan data of the patient. The tumor size data ideallywill accompany a blood-based sample provided for testing. Alternatively,some surrogate or proxy for tumor size data could be used (for example,some combination of mass spectral features alone or combined with otherserum proteins measured by alternative methods, such as ELISA). At step3804, the determination is made as to whether the tumor is “large” or“small”, again using this data. The criteria for this determinationcould take the form of criteria used to sort samples in step 3704 ofFIG. 37.

If the tumor size is “small”, then at step 3806 the sample is classifiedusing the small tumor classifier generated and stored at step 3712 ofFIG. 37, using the system of FIG. 15. That is, the blood-based sample issubject to mass spectrometry, and the mass spectrometry data is subjectto the steps shown in FIG. 15 with the classifier used forclassification being the small tumor classifier generated in FIG. 37. Atstep 3808 the class label Early or Late is assigned to the sample. Ifthe patient is identified as Late, the patient is predicted to obtainbenefit from the immune checkpoint inhibitor and have improved overallsurvival as compared to a class label of Early.

If at step 3804 the tumor size is “large”, the large tumor classifier ofFIG. 37 is then used to classify the blood-based sample of the patientusing the system of FIG. 15. At step 3812 the class label Early or Latefrom the classifier is reported. The Early and Late labels have the samemeaning as explained in the previous paragraph.

FIG. 39 is a flow chart showing a procedure 3702 of removing the fastestprogressing samples from a development set as a preliminary step ingenerating the large and small tumor classifiers of FIG. 37. At step3904, a classifier is generated over all the samples in a developmentsample set using the procedure of FIG. 8. An example of this is the fullset classifier of Example 1, described above.

At step 3906, this classifier is then used to classify all the samplesin the development sample set. Each member of the development sample setis then classified as either Early or Late. The Early and Late patientsare grouped into two groups shown at 3908 and 3910.

At step 3912, a new classifier is generated using the process of FIG. 8,with the Early patient group 3908 forming the development sample set ofFIG. 8. The process FIG. 8 is performed in an effort to split the Earlypatients into Earlier and Later sub-groups. An example of this wasdescribed at the beginning of this section of the document and theresults shown in FIGS. 34A and 34B. At step 3914, after the classifierof 3912 has been generated it is applied to all the Early samples (3908)and the resulting classifications of the Early patients into “Earlier”and “Later” sub-groups 3916 and 3918 is performed. The Earlier patients3916 are then identified and removed from the development sample set.The process of FIG. 37 then proceeds at step 3704 with the developmentsample set minus this “Earlier” sub-group of patients to produce thesmall tumor and large tumor classifiers.

From the above discussion, it will be apparent that one aspect of thedisclosed inventions is a machine (e.g., FIG. 15, 1510) programmed as aclassifier for classifying a cancer patient as likely or not likely tobenefit from an immune checkpoint inhibitor. The machine 1510 includes amemory 1514 storing parameters of a small tumor classifier and a largetumor classifier, and a reference set of class-labeled mass spectraldata for each of the small tumor classifier and the large tumorclassifier. The reference sets are obtained from blood-based samples ofother cancer patients treated with the immune checkpoint inhibitor, asexplained above. The machine further includes a processing unit (FIG.15, 1512) executing a classifier defined by the parameters stored in thememory. In a preferred embodiment, the parameters defining theclassifier for each of the small tumor classifier and large tumorclassifier include parameters defining a classifier configured as acombination of filtered mini-classifiers with drop out regularization,e.g., resulting from the procedure of FIG. 8 steps 102-150. In onepossible embodiment the mass spectral data is obtained from performingMALDI-TOF mass spectrometry on the blood-based samples and wherein eachof the samples is subject to at least 100,000 laser shots, e.g. usingthe so-called Deep MALDI methods described in Example 1.

In another aspect, a method of generating a classifier for classifyingcancer patients as likely or not likely to benefit from a drug has beendescribed, comprising the steps of: 1) obtaining a development sampleset (FIG. 8, 100) in the form of a multitude of blood-based samples; 2)conducting mass spectrometry on the development sample set (see Example1); 3) removing from the development sample set samples from patientswith a relatively fast progression of disease after commencement oftreatment by the drug; (step 3702, FIG. 37) 4) sorting the remainingsamples based on tumor size at baseline (commencement of treatment) intolarge and small tumor groups; (FIG. 37 step 3704); 5) for the smalltumor group: a) generating a small tumor classifier using the smalltumor group as an input development sample set in a classifierdevelopment exercise; (FIG. 37, step 3708) b) verifying the performanceof the small tumor classifier in classification of the members of thesmall tumor sample group; (FIG. 37, step 3710) and c) storing theparameters of the small tumor classifier; (FIG. 37, step 3712); and 6)for the large tumor group: a) generating a large tumor classifier usingthe large tumor group as an input development sample set in a classifierdevelopment exercise; (FIG. 37 step 3714) b) verifying the performanceof the large tumor classifier in classification of the members of thelarge tumor sample group; (FIG. 37 step 3716) and c) storing theparameters of the large tumor classifier (FIG. 37, 3718).

Preferably, as explained above in this section, the classifierdevelopment exercise of step 5a) and step 6a) takes the form ofimplementing the procedure of FIG. 8 steps 102-150.

In still another aspect, a method of classifying a cancer patient aslikely or not likely to benefit from a drug is contemplated. The methodincludes the steps of a) determining whether the patient has a large orsmall tumor; (FIG. 38, 3802); b) if the patient has a large tumor, usingthe large tumor classifier generated in the method described above toclassify a blood-based sample of the patient as likely or not likely tobenefit from the drug, (FIG. 38, 3806) and c) if the patient has a smalltumor, using the small tumor classifier generated as described above toclassify a blood-based sample of the patient as likely or not likely tobenefit from the drug.

In still another aspect, a method of classifying a cancer patient aslikely or not likely to benefit from a drug is contemplated. The methodincludes the step of a) conducting two classifier generation exerciseson a development set of samples which are sorted into small tumor andlarge tumor groups, resulting in the generation of a large tumorclassifier and a small tumor classifier and storing the large tumor andsmall tumor classifiers in a programmed computer (FIG. 37). Thedevelopment set of samples are blood-based samples which have beensubject to mass spectrometry. The method includes a step b) ofdetermining whether the patient has a large or small tumor, eitherdirectly from tumor measurement data or indirectly using a surrogate fortumor measurement data (FIG. 38 step 3084). The method further includesa step c) of conducting mass spectrometry on the blood-based sample ofthe cancer patient (FIG. 15, 1506, 1508). If the patient has a largetumor, the method includes a step of using the large tumor classifiergenerated in step a) and the mass spectrometry data obtained in step c)with the programmed computer to classify the patient as likely or notlikely to benefit from the drug, and if the patient has a small tumor,using the small tumor classifier generated in step a) and the massspectrometry data obtained in step c) with the programmed computer toclassify the patient as likely or not likely to benefit from the drug.

In one example, the cancer patient is a melanoma patient, and the drugis an antibody drug targeting programmed cell death 1 (PD-1). Howeverthe methods described in FIGS. 37-39 are applicable to other types ofcancer patients and drugs.

The following clauses are offered as further descriptions of thedisclosed inventions of Example 8.

1. A machine programmed as a classifier for classifying a cancer patientas likely or not likely to benefit from an immune checkpoint inhibitorcomprising;

a memory storing parameters of a small tumor classifier and a largetumor classifier, a reference set of class-labeled mass spectral datafor each of the small tumor classifier and the large tumor classifier,the reference sets obtained from blood-based samples of other cancerpatients treated with the immune checkpoint inhibitor; and

a processing unit executing either the large tumor classifier or thesmall tumor classifier defined by the parameters stored in the memory toclassify mass spectral data of a blood-based sample of the cancerpatient and assign a class label to the sample, the class labelindicating whether or not the patient is likely to benefit from theimmune checkpoint inhibitor.

2. The machine of clause 1, wherein the parameters defining theclassifier for each of the small tumor classifier and large tumorclassifier include parameters defining a classifier configured as acombination of mini-classifiers with drop out regularization.

3. The machine of clause 1 or clause 2, wherein the mass spectral datais obtained from performing MALDI-TOF mass spectrometry on theblood-based samples and wherein each of the samples is subject to atleast 100,000 laser shots.

4. A method of generating a classifier for classifying cancer patientsas likely or not likely to benefit from a drug, comprising the steps of:

1) obtaining a development sample set in the form of a multitude ofblood-based samples; 2) conducting mass spectrometry on the developmentsample set;

3) removing from the development sample set samples from patients with arelatively fast progression of disease after commencement of treatmentby the drug; and with a computer:

4) sorting the remaining samples based on tumor size at baseline(commencement of treatment) into large and small tumor groups;

5) for the small tumor group:

a) generating a small tumor classifier using the small tumor group as aninput development sample set in a classifier development exercise;

b) verifying the performance of the small tumor classifier inclassification of the members of the small tumor sample group; and

c) storing the parameters of the small tumor classifier;

and

6) for the large tumor group:

a) generating a large tumor classifier using the large tumor group as aninput development sample set in a classifier development exercise;

b) verifying the performance of the large tumor classifier inclassification of the members of the large tumor sample group; and

c) storing the parameters of the large tumor classifier.

5. The method of clause 4, wherein the classifier development exerciseof step 5a) and step 6a) comprises the procedure of FIG. 8 steps102-150.

6. A method of classifying a cancer patient as likely or not likely tobenefit from a drug, comprising the steps of

a) making an assignment of whether the patient has a large or smalltumor, either directly by tumor measurement data or indirectly using asurrogate for tumor size data;

b) if the patient has a large tumor, using the large tumor classifiergenerated in accordance with clause 4 to classify a blood-based sampleof the patient as likely or not likely to benefit from the drug, and

c) if the patient has a small tumor, using the small tumor classifiergenerated in accordance with clause 4 to classify a blood-based sampleof the patient as likely or not likely to benefit from the drug.

7. A method of classifying a cancer patient as likely or not likely tobenefit from a drug, comprising the steps of

a) conducting two classifier generation exercises on a development setof samples which are sorted into small tumor and large tumor groups,resulting in the generation of a large tumor classifier and a smalltumor classifier and storing the large tumor and small tumor classifiersin a programmed computer; wherein the development set of samples areblood-based samples which have been subject to mass spectrometry;

b) making an assignment of whether the patient has a large or smalltumor, either directly from tumor measurement data or indirectly using asurrogate for tumor measurement data;

c) conducting mass spectrometry on the blood-based sample of the cancerpatient;

d) if the patient has a large tumor, using the large tumor classifiergenerated in step a) and the mass spectrometry data obtained in step c)with the programmed computer to classify the patient as likely or notlikely to benefit from the drug, and

e) if the patient has a small tumor, using the small tumor classifiergenerated in step a) and the mass spectrometry data obtained in step c)with the programmed computer to classify the patient as likely or notlikely to benefit from the drug.

8. The method of clause 7, wherein the classifier generation exercisesof step a) comprise the procedure of FIG. 8 steps 102-150.

9. The method of clause 7 or clause 8, wherein the cancer patient is amelanoma patient, and wherein the drug is an antibody drug targetingprogrammed cell death 1 (PD-1).

Example 9 Development of an Ensemble of Classifiers from ClinicallyDifferent Classifier Development Sets and Use Thereof to Guide Treatment

Our work described in Example 8 made use of the development of differentclassifiers using clinically different development sets, i.e., with oneset from “small tumor” patients and another set from “large tumor”patients. In this Example, we extend this method of developingclassifiers more generally and describe an ensemble of differentclassifiers, each derived from clinically different development sets. Inone implementation of this Example, each development set representsdifferent tumor sizes or different proportions of tumor sizes in apopulation of melanoma patients. From this approach, we have discovereda reproducible ternary classification method and system which is betterable to identify patients who do so badly on the immune checkpointinhibitor anti-PD-1 that they might be better not taking it at all and,perhaps more importantly, others that do so well on anti-PD-1monotherapy that they might be just as well off taking anti-PD-1monotherapy rather than undergoing anti-PD1/anti-CTLA4 combinationtherapies, which incur tremendous addition expense and can have severetoxicity side effects. Later in this Example, we describe thedevelopment and implementation of an ensemble of classifiers to predictsurvival of ovarian cancer patients on chemotherapy.

Accordingly, in this section we describe a different approach toclassifier development that we have not considered before, namelydesigning the development sets of a set of classifiers to exploredifferent clinical groups, and using an ensemble of classifiers obtainedfrom such development sets to result in a, for example, ternary(three-level) classification scheme. We further describe how a classlabel produced from this ensemble of classifiers can be used to guidetreatment of a cancer patient or predict survival of a cancer patient.Those skilled in the art will appreciate that the present example ofdesigning the development set of a set of classifiers with differentclinical groups is offered by way of example and not limitation, andthat this methodology can be extended to other classifier developmentscenarios generally, including in particular other classifierdevelopments to predict patient benefit or survival from treatment withdrugs.

A. Ensemble of Classifiers for Melanoma Patient Benefit from Nivolumab

In the melanoma/nivolumab portion of this Example, the deep MALDIfeature table for the pretreatment serum samples from patients treatedwith nivolumab at the Moffitt Cancer Center (see Example 1 and AppendixA) was used for classifier development. For classifier development, the104 samples for the patients who had tumor size follow up data wereused. These 104 samples were split into two groups according to baselinetumor size: the 50 patients with smallest tumors and the 54 patientswith largest tumors. Each of these subsets was used as the developmentset to develop a classifier using the process of FIG. 8, with baggedfeature deselection and filtering of mini-classifiers on overallsurvival. These aspects have been described previously in this documentin Example 8 and Appendix F of our prior provisional application Ser.No. 62/289,587.

In addition, five other subsets of the 104 sample classifier developmentset were defined as additional or alternative development sets. Thefirst of these took the set of 50 patients with smallest tumors, dropped10 of them, and replaced these with 10 patients from the set of 54 withthe larger tumors. The second of these took the set of 50 patients withsmallest tumors, dropped 20 of them, and replaced these with 20 patientsfrom the set of 54. Three other development sets were defined extendingthis approach further. The fifth classifier was accordingly a subset ofthe original 54 large tumor size set. In this way, 5 development sets of50 patient samples were generated that contained different proportionsof patients with smaller and larger tumor sizes (80%-20%, 60%-40%,40%-60%, 20%-80%, and 0%-100%, respectively). For each of these 5development sets, classifiers were generated using the same procedure ofFIG. 8 described in detail above, i.e., each classifier was defined as afinal classifier (FIG. 8, step 150) as an ensemble average over 625master classifiers generated from 625 test/training splits of thedevelopment set used for that classifier, and each master classifier isa logistic regression combination of a multitude of mini-classifiersthat pass overall survival performance filtering criteria, andregularized by extreme drop out. Each classifier produces a binary classlabel for a sample, either Early or Late, and Early and Late have thesame clinical meaning as explained in Example 1. Hence, we obtained anensemble of 7 different classifiers (the 5 developed as described here,plus the “large” and “small” tumor classifiers described in the“Classifiers incorporating tumor size information” section, Example 8),each of which was developed on a clinically different classifierdevelopment set. It will be noted that the “large” tumor classifierdescribed in the “Classifiers incorporating tumor size information”section and the fifth of the new classifiers generated from 50 “large”tumor patients are similar, but distinct in that they were formed fromdifferent sets of patients. This ensemble of seven classifiers isreferred to herein as “IS6” or “the IS6 classifier.”

An alternative method for defining the classifier development sets withdifferent clinical groupings is as follows:

1. Order the 104 samples by tumor size.

2. Take the 50 samples with the smallest tumor size for one classifierdevelopment and the remaining 54 samples with the largest tumor foranother, just as here.

3. Define 5 other classifier development sets by

-   -   a. Dropping the 10 samples with the smallest tumor size and        taking the next 50 samples for a classifier development set.    -   b. Dropping the 20 samples with the smallest tumor size and        taking the next 50 samples for a second classifier development        set.    -   c. Dropping the 30 samples with the smallest tumor size and        taking the next 50 samples for a third classifier development        set.    -   d. Dropping the 40 samples with the smallest tumor size and        taking the next 50 samples for a forth classifier development        set.    -   e. Dropping the 50 samples with the smallest tumor size and        taking the next 50 samples for a fifth classifier development        set.

Classifiers are then developed from each of these seven classifierdevelopment sets using the procedure of FIG. 8 steps 102-150. One thenestablishes rules to combine the classification results from these sevenclassifiers, e.g., as explained below. This method of designingclassifier development sets may have similar performance as theclassifiers produced from the development sets described in the previousparagraphs, but may be more reproducible, for example in a rerunning ofthe samples or have better performance in identifying patients withparticularly good or poor outcomes.

To conduct a test on a patient's blood-based sample, the sample issubject to mass spectrometry as described above in the description ofFIG. 15. The resulting mass spectral data (integrated intensity valuesat the classification features used in the classifier developmentexercise, such as Appendix A or Appendix B) is then subject toclassification by each of the 7 classifiers in the ensemble, using thegeneral procedure of FIG. 15. Each of the 7 classifiers generates aclass label (Early/Late or similar). The set of 7 class labels is usedto define an overall classification for a test sample in accordance witha set of rules. In one particular example, samples where all classifiersin the ensemble return a good prognosis “Late” label are classified as“Good”, samples where all classifiers return a poor prognosis “Early”label are classified as “Bad”, and all other samples with mixed labelsare classified as “Other”. Of course, other monikers for this ternaryclass label scheme could be used and the particular choice of moniker isnot particularly important. The results for classifications obtainedusing this rule for combining the labels of the 7 classifiers arepresented below. Other rules for combining the 7 labels could, ofcourse, be used.

FIGS. 40A and 40B shows Kaplan-Meier plots of the results of applyingthese 7 classifiers to all 119 samples in the Moffitt Cancer Centersample set and using the rule above to generate a ternaryclassification. FIG. 40A depicts the Kaplan-Meier plot for overallsurvival, and FIG. 40B depicts the Kaplan-Meier plot for time toprogression. Samples used in the development of each classifier in theensemble are classified according to a final classifier defined from amodified majority vote (out of bag estimate) and other samples areclassified according to a final classifier defined as the average overall 625 master classifiers. Thirty samples (25%) classified as Bad and33 (28%) as Good. Patients classified as Good show very good outcomes:overall survival plateaus at 60% and time to progression at 30%. Incontrast, the patients classified as Bad demonstrate very poor outcomes,with 36% survival at one year and only 6% at two years, and 10%progression-free at 6 months and 7% at one year.

When the ensemble of classifiers was applied to a validation set of massspectral data from blood-based samples obtained from 30 patients alsotreated with anti-PD1 antibodies, the test showed similar performance.This is shown in FIG. 41, which is a Kaplan-Meier plot for overallsurvival by the classifications obtained by the ensemble of 7classifiers for the 30 samples in the anti-PD1 treated validationcohort. Thirteen patients (43%) were classified as Good and six patients(20%) as Bad.

The high proportion of patients with durable long term responses totherapy and long overall survival in the Good group is similar to theefficacy reached for patients treated with the combination of nivolumaband ipilimumab, a newly approved therapy for melanoma. This combinationtherapy, however, is not only extremely expensive (list price for a yearof treatment being greater than $250,000, see also Leonard Saltz, Md.,at ASCO 2015 plenary session: “The Opdivo+Yervoy combo is priced atapproximately 4000× the price of gold ($158/mg)”), but also hassignificant toxicities associated with it. The excellent performance ofpatients in the Good group indicates that within this group patients maynot need to be treated with the combination of an anti-PD-1 agent and ananti-CTLA4 agent, but may in fact be likely to achieve similar outcomeswith reduced risks of severe toxicities with the anti-PD-1 agent, suchas nivolumab, alone.

In addition, the very poor outcomes of the Bad group, indicate that thelikelihood of these patients receiving durable benefit from nivolumab orother anti-PD-1 agents is extremely low. Such patients may be directedtowards less costly therapies of similar efficacy, therapies of betterefficacy in this population, if they can be found, or to a clinicaltrial or palliative care.

The classification label of “Other” produced by the ensemble ofclassifiers in this Example is also useful, and in this particularapplication a ternary classifier is quite appropriate and even desirablein terms of guiding treatment decisions in melanoma: The Goods shouldget nivolumab monotherapy, the Others would be good candidates fornivolumab plus ipilimumab (as, at least in the Moffitt set, they appearto get some benefit from nivolumab, but could probably do better on thecombination therapy, as combination therapy demonstrates better outcomein an unselected population), and the Bads do not seem to benefit at allfrom nivolumab and probably would not be saved by addition of ipilimumab(anti-CTLA4 antibody, Yervoy™), and so should be directed to some otherkind of therapy, or possibly clinical trials or palliative care.

As noted above, the rules defined for the ensemble of classifiers canvary and in one possible embodiment a majority vote over the 7classifiers in the ensemble could be used to assign a class label to atest sample. In this particular Example, the majority vote gives a classlabel, either Early or Late. The classification produced by the majorityvote are very close to the class labels produced by the full-setapproach 1 classifier of Example 1 (“IS2” herein), which is perhaps notsurprising since both are generated over development sets covering awide range of tumor sizes.

The ensemble of seven tumor size classifiers created from developmentsubsets drawn with different distributions of baseline tumor size(referred to herein as “IS6”)) was also applied to pretreatment serumsamples collected from two patient cohorts: 30 patients treated withanti-PD-1 therapies in an observational study (the validation set usedfor IS6 and shown in FIG. 41 and used as the independent validation setfor classifiers 1 and 2 of our provisional application Ser. No.62/191,895 filed Jul. 13, 2015) and 21 patients treated with thecombination of the anti-PD-1 agent, nivolumab, with the anti-CTLA4agent, ipilimumab. Both cohorts were collected at a single institutionas part of an observational study.

It had been noted that IS6 identifies a group of patients withespecially good outcome when treated with anti-PD-1 agents. Asidentifying this group of very good performing patients was the aimhere, instead of plotting the three outcome groups of IS6, we look atthe best outcome group, the “Good” group and we combine the other twoclassification groups, intermediate prognosis group (“Other”) and pooroutcome group (“Bad”), into a single group which we call “Not Good”(i.e., “Not Good”=“Other”+“Bad”). When the Kaplan-Meier curves foroverall survival for these two cohorts of patients are plotted by “Good”versus “Not Good” on the same plot, one obtains results shown in FIG.42.

It can be seen from FIG. 42 that the difference in outcomes between“Good” and “Not Good” is smaller for patients treated with thecombination therapy (ipilimumab+nivolumab) than for patients treatedwith nivolumab alone. More importantly, there is no evidence thatpatients classified as “Good” receive benefit from the addition ofipilimumab to nivolumab therapy. Although this comparison should be madewith some caution as these two cohorts are not two arms of a randomizedtrial, both cohorts were collected from patients treated at the sameinstitution, and, as significant toxicities can be experienced with thecombination therapy, it might be expected that any bias between thepopulations would be in favor of better prognostic factors for patientstreated with combination therapy. These results would indicate that itmay be possible to identify, using the IS6 classifier or other similarperforming classifiers (for example the classifiers of Example 6constructed from mass spectral feature subsets associated with specificprotein functions), a group of patients identified with the class label“Good” or the equivalent who would achieve similar outcomes withnivolumab as with the combination of nivolumab and ipilimumab. Hencethese patients would receive no significant benefit from receivingipilimumab in addition to nivolumab, while combination therapy for thesepatients would still incur considerable extra cost and expose patientsto significantly higher risk of severe toxicities and side-effects. Onthe other hand those patients whose serum is classified as Not Good bythe IS6 classifier (i.e., where any one of the ensemble of classifiersreturns the Early class label), such patient would likely benefit fromthe addition of ipilimumab to nivolumab as compared to nivolumabmonotherapy. As noted above, the IS6 classifier of Example 9 providessimilar classification results to the classifier developed usingfeatures selected according to their association with their biologicalfunction of acute response and wound healing in Example 6.

Generalizing this discovery (and considering the content of Example 10below, especially the discussion of FIGS. 50C and 50D, wherein wedisclose that classifiers developed from mass spectral featuresassociated with biological functions have similar classifier performanceto IS6), we can say that we described a method of guiding melanomapatient treatment with immunotherapy drugs, comprising the steps of a)conducting mass spectrometry on a blood-based sample of the patient andobtaining mass spectrometry data; (b) obtaining integrated intensityvalues in the mass spectrometry data of a multitude of mass-spectralfeatures; and (c) operating on the mass spectral data with a programmedcomputer implementing a classifier; wherein in the operating step theclassifier compares the integrated intensity values with feature valuesof a reference set of class-labeled mass spectral data obtained fromblood-based samples obtained from a multitude of other melanoma patientstreated with an antibody drug blocking ligand activation of programmedcell death 1 (PD-1) with a classification algorithm and generates aclass label for the sample. The class label “Good” or the equivalent(e.g., Late in the description of Example 10) predicts the patient islikely to obtain similar benefit from a combination therapy comprisingan antibody drug blocking ligand activation of PD-1 and an antibody drugtargeting CTLA4 and is therefore guided to a monotherapy of an antibodydrug blocking ligand activation of PD-1 (e.g., nivolumab), whereas aclass label of “Not Good” or the equivalent (e.g., Early in thedescription of Example 10) indicates the patient is likely to obtaingreater benefit from the combination therapy as compared to themonotherapy of an antibody drug blocking ligand activation of programmedcell death 1 (PD-1) and is therefore guided to the combination therapy.

In one embodiment the mass spectral features include a multitude offeatures listed in Appendix A, Appendix B or Appendix C, or featuresassociated with biological functions Acute Response and Wound Healing.In preferred embodiments the classifier is obtained from filteredmini-classifiers combined using a regularized combination method, e.g.,using the procedure of FIG. 8 or FIG. 54. The regularized combinationmethod can take the form of repeatedly conducting logistic regressionwith extreme dropout on the filtered mini-classifiers. In one examplethe mini-classifiers are filtered in accordance with criteria listed inTable 10. As disclosed in this example, the classifier may take the formof an ensemble of tumor classifiers combined in a hierarchical manner.In the illustrated embodiment if any one of the tumor classifiersreturns an Early or the equivalent label the Not Good or equivalentclass label is reported, whereas if all the tumor classifiers return aLate class label the Good or equivalent class label is reported.

In this method the relatively greater benefit from the combinationtherapy label means significantly greater (longer) overall survival ascompared to monotherapy.

In another aspect the reference set takes the form of a set ofclass-labeled mass spectral data of a development set of samples havingeither the class label Early or the equivalent or Late or theequivalent, wherein the samples having the class label Early arecomprised of samples having relatively shorter overall survival ontreatment with nivolumab as compared to samples having the class labelLate.

In preferred embodiments the mass spectral data is acquired from atleast 100,000 laser shots performed on the sample using MALDI-TOF massspectrometry.

In one embodiment, as indicated in Examples 6 and 10 the mass-spectralfeatures are selected according to their association with at least onebiological function, for example sets of features which are associatedwith biological functions Acute Response and Wound Healing.

B. Ensemble of Classifiers for Predicting Ovarian Cancer Patient Benefitfrom Platinum-Based Chemotherapy

This Example also discloses the development of classifiers which predictin advance whether an ovarian cancer patient is likely to beplatinum-refractory or platinum-resistant in treatment of the ovariancancer with platinum-based chemotherapy. In one embodiment, theclassifier includes: a) a machine-readable memory storing a referenceset of class-labeled mass spectral data obtained from blood-basedsamples of other ovarian cancer patients treated with the platinum-basedchemotherapy. The mass spectral data is in the form of a feature tableof intensity values of a multitude of mass spectral features. The classlabels are of the form Early or the equivalent, indicating that thesample was from a patient who did relatively poorly on platinum-basedchemotherapy, or Late or the equivalent, indicating that the sample wasfrom a patient that did relatively well on platinum-based chemotherapy.The classifier also includes b) a programmed computer implementing aclassification algorithm comparing mass spectral data of a sample to betested with the reference set and generating a class label for thesample to be tested.

In particular, the classification algorithm implements a hierarchicalmulti-level classification in series including classification at atleast a first level (“Classifier A” in the following description) and asecond level (“Classifier B” in the following description). Theclassification algorithm at the first level produces a class label ofEarly or Late or the equivalent.

The class label Late or the equivalent identifies patients as beinglikely to not be platinum-refractory or platinum-resistant in treatmentof the ovarian cancer with platinum-based chemotherapy. If the classlabel assigned at the first level is Early or the equivalent, theclassification algorithm proceeds to the second level. The classifier atthe second level uses a subset of the reference set in the form ofpatients identified with the class label Early or the equivalent andfurther stratifies into Early and Late class labels (or Earlier or Laterlabels, or the equivalent). The classification algorithm at the secondlevel generates a class label of Bad or the equivalent identifyingpatients as likely to perform very poorly on platinum-basedchemotherapy, i.e., be platinum-refractory or platinum-resistant.

In one embodiment, the hierarchical multi-level classification includesa third classification level (“Classifier C” in the followingdescription), wherein a class label assigned at the third classificationlevel is used to identify patients as being likely to have particularlygood outcomes on the platinum-based chemotherapy, and is applied tothose samples which are assigned the Late (or equivalent) class label bythe first level classifier.

We have found that is desirable to develop classifiers from differentclinical sub-groups within a classifier development set used to generatethe first level classifier. For example, the classifiers at the firstclassification level can be developed from one or more differentclinical subgroups, for example four different classifiers C1, C2, C3,and C4, each developed from the different clinical sub-groups. In theovarian cancer scenario, these clinical subgroups can take the form of:C1: a subset of patients with non-serous histology or serous histologytogether with unknown FIGO (a cancer scoring system) score; C2: a subsetof patients not used to develop Classifier C1 (e.g., patients withserous histology and known FIGO score); C3: a subset of patients withresidual tumor after surgery; C4: a subset of patients with no residualtumor after surgery.

A further example of this methodology will be described below inconjunction with a set of ovarian cancer patient samples.

Samples

A set of 165 blood-based (serum) samples from an observational trial ofpatients with ovarian cancer were available. Patients underwent surgeryfollowed by platinum-based chemotherapy. Samples were taken at the timeof surgery (in advance of treatment with platinum-based chemotherapy).This cohort has already been described in Example 4 and the baselinecharacteristics of the cohort were shown in Table 37 above.

Kaplan-Meier plots for disease-free-survival (DFS) and overall survival(OS) for the cohort of 138 patients with baseline samples and acquiredspectra were shown in FIGS. 27A and 27B.

Sample preparation, spectral acquisition and spectral data processingwere similar to the description in Example 1 and so a detaileddescription here is omitted.

Turning now to FIG. 54A, the classifier development process will bedescribed in further detail in the context of the ovarian/platinumchemotherapy classifier.

The subset of 129 patients with available DFS data and DFS known to bein excess of 1 month were selected from the whole cohort of 138patients. This subset was then split in half stratified on outcome andtaking account of how features were related to outcome within each half,as explained in Appendix B of our prior provisional application62/319,958, to produce a matched development and internal validationset. The resulting development set of 65 samples was used to develop andinitial or first level classifier, referred to as Classifier A, in thefollowing discussion. It will be appreciated that it would also bepossible to develop a classifier from the whole cohort, e.g., wherethere is another cohort of samples available for a validation exercise.

At step 302, a definition of the two class labels (or groups) for thesamples in the development set 300 was performed. While some preliminaryapproaches used for classifier development employed well-defined classlabels, such as response categories or chemo-resistance (yes/no), theseproved to be unsuccessful. All approaches discussed in this section ofthe Example 9 make use of time-to-event data for classifier training. Inthis situation class labels are not obvious and, as shown in FIGS. 54Aand 54B, the methodology uses an iterative method to refine class labels(loop 346) at the same time as creating the classifier. At step 302, aninitial guess is made for the class labels. Typically the samples aresorted on either DFS or OS and half of the samples with the lowesttime-to-event outcome are assigned the “Early” class label (early deathor progression, i.e. poor outcome) while the other half are assigned the“Late” class label (late death or progression, i.e. good outcome).Classifiers (step 330) are then constructed using the outcome data andthese class labels for many different training sets (312) drawn from thedevelopment set and the associated test sets (310) classified. The classlabels of samples which persistently misclassify when in the test setacross the multiple training/test set splits (loop 335) are flipped (344and loop 346) and the resulting new set of class labels are then usedfor a second iteration of the classifier construction step. This processis iterated until convergence. The Early and Late groups are shown at304 and 306.

At step 308, the Early and Late samples of the development set (300) arethen divided randomly into training (312) and test sets (310). Thetraining set (312) is then subject to steps 320, 326 and 330. In step320, many k-nearest neighbor (kNN) mini-classifiers (mCs) that use thetraining set as their reference set are constructed (defined) usingsubsets of features from the reduced set of spectral featuresidentified. For these investigations, all possible single features andpairs of features were examined (s=2); however, one could choose toexplore the reduced feature space more deeply using triplets (s=3) oreven higher order combinations of features. All approaches described inthis section of Example 9 all use k=9, but other values of k such as 7or 11 could be considered.

In step 326 a filtering process was used to select only thosemini-classifiers (mC) that had useful or good performancecharacteristics. This can be understood in FIG. 54A by the spectra 324containing many individual features (shown by the hatched regions) andthe features alone and in pairs are indicated in the reduced featurespace 322. For some of the kNN mini-classifiers, the features (singly orin pairs) perform well for classification of the samples and suchmini-classifiers are retained (indicated by the “+” sign in FIG. 54A at328) whereas others indicated by the “−” sign are not retained.

To target a final classifier that has certain performancecharacteristics, these mCs were filtered as follows. Each mC is appliedto its training set and performance metrics are calculated from theresulting classifications of the training set. Only mCs that satisfythresholds on these performance metrics pass filtering to be usedfurther in the process. The mCs that fail filtering are discarded. Forthis project hazard ratio filtering was used. For hazard ratiofiltering, the classifier was applied to the training set. The hazardratio for OS was then calculated between the group classified as Earlyand the rest classified as Late. The hazard ratio had to lie withinspecified bounds for the mC to pass filtering.

At step 330, we generated a master classifier (MC) for each realizationof the separation of the development set into training and test sets atstep 308. Once the filtering of the mCs was complete, at step 332 themCs were combined in one master classifier (MC) using a logisticregression trained using the training set class labels, step 332. Tohelp avoid overfitting the regression is regularized using extreme dropout with only a small number of the mCs chosen randomly for inclusion ineach of the logistic regression iterations. The number of dropoutiterations was selected based on the typical number of mCs passingfiltering to ensure that each mC was likely to be included within thedrop out process multiple times. All approaches outlined in this sectionof Example 9 left in 10 randomly selected mCs per drop out iteration andused 10,000 drop out iterations.

At step 334, we evaluated the performance of the MC arrived at in step332 and its ability to classify the test set of samples (310). With eachiteration of step 320, 326, 330, 334 via loop 335 we evaluate theperformance of the resulting MC on its ability to classify the membersof the test set 310. In particular, after the evaluation step 334, theprocess looped back via loop 335 to step 308 and the generation of adifferent realization of the separation of the development set intotraining and test sets. The process of steps 308, 320, 326, 330, 332,334 and looping back at 335 to a new separation of the development setinto training and test sets (step 308) was performed many times. The useof multiple training/test splits avoids selection of a single,particularly advantageous or difficult, training set for classifiercreation and avoids bias in performance assessment from testing on atest set that could be especially easy or difficult to classify.

At step 336, there is an optional procedure of analyzing the data fromthe training and test splits, and as shown by block 338 obtaining theperformance characteristics of the MCs from each training/test set splitand their classification results. Optional steps 336 and 338 were notperformed in this project.

At step 344, we determine if there are samples which are persistentlymisclassified when they are present in the test set 310 during the manyiterations of loop 335. If so, we flip the class label of suchmisclassified samples and loop back in step 346 to the beginning of theprocess at step 302 and repeat the methodology shown in FIGS. 54A and54B.

If at step 344 we do not have samples that persistently misclassify, wethen proceed to step 350 and define a final classifier in one of severalways, including (i) a majority vote of each master classifier (MC) foreach of the realizations of the separation of the development set intotraining and test sets, or (ii) an average probability cutoff

The output of the logistic regression (332) that defines each MC is aprobability of being in one of the two training classes (Early or Late).These MC probabilities can be averaged to yield one average probabilityfor a sample. When working with the development set 300, this approachis adjusted to average over MCs for which a given sample is not includedin the training set (“out-of-bag” estimate). These average probabilitiescan be converted into a binary classification by applying a threshold(cutoff). During the iterative classifier construction and labelrefinement process, classifications were assigned by majority vote ofthe individual MC labels obtained with a cutoff of 0.5. This process wasmodified to incorporate only MCs where the sample was not in thetraining set for samples in the development set (modified, or“out-of-bag” majority vote). This procedure gives very similarclassifications to using a cutoff of 0.5 on the average probabilitiesacross MCs.

After the final classifier is defined at step 350, the processoptionally continues with a validation step 352 in which the finalclassifier defined at step 350 is tested on an internal validation setof samples, if it is available. In the present example, the initial setof samples was divided into a development set (300) and a separateinternal validation set, and so this validation set existed and wassubject to the validation step 352. See FIGS. 55A and 55B for theKaplan-Meier plots for DFS and OS for the development and validationsets. Ideally, in step 354 this final classifier as defined at step 350is also validated on an independent sample set.

FIG. 54A shows a step 52 of deselection of features from an initialfeature space to a reduced feature space. This was done using a baggedfeature deselection procedure which is described in our priorprovisional application Ser. No. 62/319,958, see FIGS. 3 and 4 thereof,the details of which are omitted for the sake of brevity.

Classifier A Development

Initial new classifier development was performed using the process ofFIGS. 54A and 54B described in detail above, using 129 samples. This wasa reduced set including only patients with DFS greater than 1 month. Thesample number allowed for a split into a development set and an internalvalidation set for classifier development. The split into developmentand validation sets was stratified by censoring of DFS and OS. Theassignment of individual samples to validation or development sets isshown and described in detail in Appendix A and Appendix B,respectively, of our prior provisional application Ser. No. 62/319,958.The development set had 65 patients and validation set had 64 patients.The clinical characteristics are listed for the development andvalidation split in table 61.

TABLE 61 Baseline characteristics of patients with available spectrasplit into development (n = 65) and internal validation (n = 64) setsDevelopment set Validation set n (%) n (%) Histology serous 47 (72) 47(73) non-serous 18 (28) 17 (27) Veri Strat Good 50 (77) 53 (83) LabelPoor 15 (23) 11 (17) FIGO NA 16 (25) 21 (33) 1  6 (9)  7 (11) 2  1 (2) 2 (3) 3 30 (46) 21 (33) 4 12 (18) 13 (20) Histologic NA  1 (2)  1 (2)Grade 1  2 (3)  5 (8) 2 25 (38) 23 (36) 3 37 (57) 35 (55) Metastatic yes 9 (14)  7 (11) Disease no 56 (86) 57 (89) Residual yes 27 (42) 20 (31)Tumor no 38 (58) 44 (69) Age Median 57 (18-88) 59 (20-83) (range)This development set of samples was used with its associated clinicaldata in the procedure of FIGS. 54A and 54B, as described above, togenerate a classifier (Classifier A) able to stratify patients into twogroups with better (“Late”=late progression) and worse (“Early”=earlyprogression) outcomes. The features used in Classifier A (the reducedfeature space created by feature deselection in the final iteration ofloop 346 FIG. 54A) are listed in Appendix E of our prior provisionalapplication Ser. No. 62/319,958. Performance of the classifier wasassessed within the development set using out-of-bag estimates aspreviously described. The classifier was then applied to the validationset to assess its performance in an internal validation set not used atall in the development of the classifier (352 in FIG. 54B).

Performance of Classifier A

The performance of the Classifier A was assessed using Kaplan-Meierplots of DFS and OS between samples classified as Early and Late,together with corresponding hazard ratios (HRs) and log-rank p values.The results are summarized in tables 62 and 63.

TABLE 62 Performance summary for Classifier A OS OS DFS DFS log- Medianlog- Median #Early/ OS HR rank (Early, DFS HR rank (Early, #Late (95%CI) p Late) (95% CI) p Late) Development 25/40 2.76 0.002 23, not 2.440.004 15, 51 (1.54-6.82) reached (1.42-5.77) (Months) (Months)Validation 24/40 2.54 0.005 28, not 2.31 0.008 15, 41 (1.44-6.67)reached (1.33-5.69) (Months) (Months)

TABLE 63 Performance summary for classifier run on all the 138* samplesDFS DFS log- Median #Early/ OS HR OS log- OS Median DFS HR rank (Early,#Late (95% CI) rank p (Early, Late) (95% CI) p Late) Whole set 54/842.65 <0.001 26, not 2.44 <0.001 14, (1.89-5.21) reached (1.80-4.72) 48(Months) (Months) *Note: 2 samples of the 138 samples did not have DFStime-to-event data.Kaplan-Meier plots corresponding to the data in table 62 are shown inFIGS. 56A-56D and data in table 63 are shown in FIGS. 57A and 57B. Theclassifications per sample are listed in Appendix C of our priorprovisional application Ser. No. 62/319,958.

Of note for prediction of chemo-resistance: DFS is 74% at 6 months inthe Early group, compared with 93% in the Late group and at 12 monthsDFS is 58% in the Early group compared with 80% in the Late group. Of 14patients with DFS of 4 months or less 9 (64%) are classified as Earlyand of the 20 patients with DFS of 6 months or less 14 (70%) areclassified as Early, see table 64.

TABLE 64 DFS before 4 months, 6 months, 10 and 12 months Early Late Pvalue DFS ≦ 4 months 9 5 0.079 No DFS ≦ 4 months 44 77 DFS ≦ 6 months 146 0.005 No DFS ≦ 6 months 39 76 DFS ≦ 10 months 19 13 0.007 No DFS ≦ 10months 32 68 DFS ≦ 12 months 22 16 0.006 No DFS ≦ 12 months 29 63Baseline clinical characteristics are summarized by classification groupin table 65.

TABLE 65 Clinical characteristic by classification group when run on 138samples Early set Late set (N = 54) (N = 84) n (%) n (%) P valueHistology serous 45 (83)   55 (65)   0.031 non-serous  9 (17)   29 (35)VeriStrat Good 27 (50)   83 (99) <0.001 Label Poor 26 (48)   1 (1)Indeterminate  1 (2)   0 (0) FIGO 1  0 (0)   13 (15) <0.001

2  1 (2)   2 (2) 3 21 (39)   33 (39) 4 20 (37)   9 (11) NA 12 (22)   27(32) Histologic NA  0 (0)   2 (2)   0.379* Grade 1  1 (2)   6 (7)    220 (37)   33 (39)    3 33 (61)   43 (51)    Metastatic yes 14 (26)   6(7)   0.003 Disease no 40 (74)   78 (93) Residual yes 38 (70)   15 (18)<0.001 Tumor no 16 (30)   69 (82) Age Median 60 (35-88) 57.5 (18-83)(range) *1 + 2 vs 3,

1-3 vs 4Test classification is significantly associated with histology, FIGOscore and presence of metastatic disease. Table 66 shows the results ofmultivariate analysis of OS and DFS for the whole cohort.

TABLE 66 Multivariate analysis of the whole cohort OS DFS Covariate HR(95% CI) P value HR (95% CI) P value Early vs Late 1.68 (0.99-2.84)0.054 1.63 (0.97-2.72) 0.064 FIGO 1-3 vs 4 0.33 (0.18-0.59) <0.001 0.46(0.26-0.82) 0.009 FIGO NA vs 4 0.46 (0.24-0.87) 0.018 0.67 (0.35-1.28)0.220 Non-Serous vs Serous 0.88 (0.47-1.64) 0.681 0.86 (0.47-1.57) 0.621Tumor Residual (yes vs 2.40 (1.38-4.16) 0.002 2.07 (1.23-3.49) 0.006 no)Test classification retains a trend to significance as a predictor of OSand DFS when adjusted for known prognostic factors.

Second Classifier Development (“Classifier B”)

While the performance of Classifier A was quite promising, we hoped tobe able to improve performance. In particular we have been successful inisolating subgroups of patients who exhibit particularly poor outcomesby taking the subgroup of patients who are classified as Early by aninitial classification and further stratifying within this population byusing this subgroup to train a second, follow-up classifier. Thisapproach was used to create Classifier B.

This classifier was developed using the samples that had been classifiedas “Early” from either the development set (n=25) or the validation set(n=24) by Classifier A, with the addition of the 9 samples from patientswith exceptionally poor outcomes (DFS less than 2 months) that were notused in the development of Classifier A. This subset of samples withassociated clinical data was used in the classifier developmentprocedure of FIGS. 5A and 5B as explained above to create a newclassifier, Classifier B, again assigning each sample in the reduceddevelopment set one of two classifications, “Early” or “Late”. To avoidconfusion with the Early and Late classification labels assigned by theClassifier A, we can refer to these labels as “Earlier” or “Later”. Theparticular choice of moniker is not particularly important. What isimportant is that these Early, poor performing patients identified byClassifier A, are further stratified by Classifier B into two groups,one performing relatively better (Late or Later) and another group thatperforms particularly poorly (Early or Earlier). The features used inClassifier B (the reduced feature space created by feature deselectionin the final iteration of loop 346 FIG. 54) are listed in Appendix E ofour prior provisional application Ser. No. 62/319,958. In particular,this classifier was able to split the patients in its development setinto two groups with better and worse DFS and OS, as shown in theKaplan-Meier plots of FIGS. 58A and 58B. Twenty eight of the 58 samplesused in development were classified as Early. Note in FIGS. 58A and 58Bthat those samples classified by Classifier B as Earlier have muchpoorer OS and DFS than those patients classified as Later.

The procedure we used for generating Classifier B is illustrated in flowchart form in FIG. 59 as process 902. At step 904, we used Classifier Ato generate Early or Late labels for all the samples in the entiredevelopment set. At step 906 we sorted out all the Early samples. Atstep 908 we made an initial label assignment of either Earlier or Laterfor this subset of samples based on DFS and OS data in performing step302 of the classifier development process of FIG. 54. At step 910 wethen repeated the classifier generation method of FIG. 54A-54B on thissubset of samples as the development set (augmented by 9 samples that wehad decided not to use in development or validation sets for classifierA as their DFS was one month or less). The process generated a new finalclassifier (step 350), the parameters of which were saved at step 912.These parameters include the identification of the set of samples usedfor classifier development, the features passing filtering in theminiClassifiers, the miniClassifiers definitions, the logisticregression weights computed in step 332, the value of k in theminiClassifiers, and the definition of the final classifier at step 350.

Third Classifier Development “Classifier C”

We have been successful in isolating subgroups of patients whodemonstrate particularly good outcomes by identifying clinicallydistinct subgroups of the patient cohort and developing a classifier, asdescribed above in FIGS. 54A and 54B, for each distinct subgroup. Weapply these multiple classifiers to a test sample and if the samplealways classifies as “Late” with each of the multiple classifiers weassign an overall classification of “Good” to indicate a likelihood of aparticularly good prognosis. This approach was used to create ClassifierC (which is composed of the multiple classifiers C1, C2, C3, and C4).

Classifier C was created using all 138 available samples. Four differentclassifiers (C1, C2, C3, and C4) were generated using the same procedureof FIGS. 54A and 54B as was used for Classifier A and Classifier B, withdevelopment sets chosen to be clinically distinct subsets of the totalcohort of 138 patients. Given the available clinical data, histology andpresence/absence of residual tumor after surgery were chosen todetermine the clinically distinct subsets.

-   -   Classifier C1 was developed on the subset of 60 patients with        non-serous histology or serous histology together with unknown        FIGO score.    -   Classifier C2 was developed on the subset of 78 patients not        used to develop Classifier C1. These patients all had serous        histology, and a known FIGO score.    -   Classifier C3 was developed on the subset of 53 patients with        residual tumor after surgery.    -   Classifier C4 was developed on the subset of 85 patients with no        residual tumor after surgery.

Note: when ovarian cancer is diagnosed it is staged (usually using FIGOscore) and given a histological type and grade by a pathologist fromtumor tissue taken at surgery (biopsy is generally avoided in ovariancancer as it is better to remove the tumor(s) whole). The predominanthistological subtype for ovarian cancer is serous. Other less commontypes include mucinous, endometriod, and clear cell. These last 3 arecombined into the “non-serous” histology type. Non-serous histologycompared with serous histology is a positive prognostic factor.

As the goal of Classifier C was to be able to identify ovarian cancerpatients that would likely do particularly well on platinumchemotherapy, the selection of the clinical subgroups for individualgeneration of classifiers was done with the idea of selecting clinicallydifferent subgroups known to have different prognosis and seeing whichpatients always do well. In particularly, for a patient to performreally well, ideally you they should be classified as performing well incomparison with all possible clinically distinct population. Hence, itdoesn't really matter how one selects the clinical subgroups, but theyneed to be clinically different and should ideally be clearly differentin terms of patient prognosis. It would be possible in some situationsthat one could select clinical subgroups based on tumor size. Here, welooked at the clinical characteristics that we had available which weknew were prognostic factors (FIGO score, histology, residual tumor). Wesplit the cohort into two for each of these factors, and made 2classifiers, one on each subset. Then we looked to see whether theresulting classifications were very different depending on the twoclassifiers for each factor. It turned out that histology and residualtumor worked best and complemented each other and adding in the FIGOscore based classifiers didn't change the classifier performance much.The original plan was to then make more subgroups using one or more ofthese factors. But, we discovered that just using the two classifiersfor each of histology and residual tumor already worked very well, so wedidn't pursue further clinical subgroups, but in theory it wouldcertainly be possible to do so. One might get the most advantage fromthis method by looking at the two most different subgroups e.g. all noresidual tumor vs all residual tumor. Adding in further subgroups withadmixtures of the two extreme groups, does not add so much in terms ofprinciple refinement of the groups, but it does protect against thepossibility of getting results in one of the two extreme subgroupclassifiers that are just due to the particularities of the developmentset and not really due to the clinically different subsets. This isalways a danger when, as usual, we have relatively low numbers ofpatient samples to work with, and having more than two subgroups perclinical characteristic might help to avoid this.

All four classifiers were created to split samples into two classes,Early and Late. Each classifier was then applied to all 138 samples.Classifications of samples within the development set of each classifierwere generated using out-of-bag estimates. This provided fourclassifications for each sample, one from each of the four classifiers,C1, C2, C3, and C4. Samples receiving a “Late” classification from allfour classifiers were assigned a “Good” classification label.

The above method for generating Classifier C is illustrated in flowchart form in FIG. 60 as procedure 1102. At step 1104, one defines up toN clinically distinct subgroups of patients from the classifierdevelopment set, e.g., by inspection of the clinical data that isassociated with each of the samples. The development set is then dividedinto subsets 1, 2, 3, . . . N, where N is typically an integer of 2 ormore. At step 1108, we repeat the classifier development process (FIGS.54A and 54B) for each of the subsets 1 . . . N. In the present ovariancontext, N=4 and the subgroups are as identified above. At step 1110,the final classifier resulting at step 350 from procedure of FIGS. 54Aand 54B is saved for each of the subsets, resulting in classifiers C1,C2, . . . CN. The features used in Classifiers C1, C2, C3, and C4 (thereduced feature space created by feature deselection in the finaliteration of loop 346, FIG. 54A, for each of the four classifiers) arelisted in Appendix E of our prior provisional application Ser. No.62/319,958.

The schema or composition of Classifier C is shown in FIG. 61. A testspectrum 1200 (feature values for the features used for classificationof a test sample) is supplied to each of the classifiers 1202, 1204,1206 and 1208. Each classifier generates a label, either Early or Latein this example. At step 1210, a check is made to determine whether eachclassifier C1 . . . C4 produced the Late class label. If so, the classlabel Good is reported at step 1214. In the present context, this classlabel indicates that the ovarian cancer patient is predicted to have aparticularly good outcome on platinum chemotherapy. Conversely, if atstep 1210 the classifiers are not unanimous in producing the Late classlabel, the class label Other (or the equivalent) is reported at step1218. It will be noted that the Classifier C of FIG. 61 (strictlyspeaking, the set of parameters stored in memory including referenceset, logistic regression weights, identification of features forminiClassifiers, etc.) includes not only the underlying classifiers C1 .. . . C4 defined per FIG. 54A but also the logic for comparing theresults of each of the classifiers C1 . . . C4 and generating a final aclass label depending on the results of the classifiers C1 . . . C4.

Hierarchical Combination of Classifiers

Classifiers A, B and C can be used in a hierarchical or orderedcombination. For example, Classifier A can be used to initially classifya test sample, and if the Classifier A produces an Early class labelthen Classifier B is employed to generate a class label. If Classifier Bproduces an Early or Earlier label, the patient providing the samples isexpected to perform particularly poorly on the platinum chemotherapy(platinum refractory or platinum resistant). If Classifier A producesthe Late class label, the patient is predicted to perform well onplatinum chemotherapy.

As another example, Classifier A and C can be used in combination.Classifier A can be used to initially classify a test sample, and if theClassifier A produces an Early class label the patient is predictedperform particularly poorly on the platinum chemotherapy (platinumrefractory or platinum resistant). If Classifier A produces the Lateclass label, the patient sample is then subject to classification byClassifier C. If Classifier C produces a Late class the patientproviding the samples is expected to perform very well on platinumchemotherapy and the Good class label is returned. If Classifier Cproduces an Early class label, the Other class label can be returned.The meaning and usage of the Other class label is explained below.

Furthermore, Classifiers A, B and C can also be used in a hierarchicalor ordered manner as shown in FIG. 62. A test sample is first classifiedby Classifier A, step 1302. If it classifies as Early (step 1304), it isthen classified by Classifier B (1306). At step 1308 the class labelproduced by Classifier B is inspected. If Classifier B also returns anEarly classification (branch 1310) an overall label of “Bad” is returned(poor prognosis, platinum refractory or platinum resistant). IfClassifier B returns a Late classification (branch 1316) or Classifier Areturns a Late classification (branch 1314) the sample is classified byClassifier C (1318). Classifier C is trained to identify patientsperforming particularly well on the therapy. At step 1320 a check ismade of the classification label produced by Classifier C. If ClassifierC returns a “Late” classification (branch 1322), an overall “Good”classification is assigned to the sample (1324). If Classifier C doesnot return a “Late” classification (branch 1326), the sample receives anoverall “Other” classification (1328).

A variation of the construction of the final classifier of FIG. 62 isshown in FIG. 63. The sample is classified initially by Classifier A(1402). At step 1404, a check is made of the classification label. Ifthe label is Early, the sample is classified by Classifier B. At step1408 a check is made of the class label assigned by Classifier B. IfClassifier B also produces a class of Early (branch 1410) the classlabel of Bad is assigned 1412. If at step 1404 the Classifier A producedthe Late class label (1414), or if Classifier B produced the Late classlabel, the sample is classified by the four third-level classifiers1418A, 1418B, 1418C and 1418D, in this example corresponding to the C1 .. . C4 classifiers explained above. At step 1420, a check is made to seeif each of the four classifiers produced a Late class label. If so,branch 1422 is taken and the Good class label is reported. If at step1420 the four classifiers do not all produce the Late class label,branch 1426 is taken and the Other class label is reported.

As was the case with the classifier construction of FIG. 61, the “finalclassifier” shown in FIGS. 62 and 63 is a combination of the individualclassifiers A, B and C (or C1 . . . C4 in FIG. 63), plus a set oflogical instructions to inspect the class labels produced by theclassifiers (including subgroup classifiers) and assign the final classlabels as shown in the figures.

Results for Final Classifier Constructed in Accordance with FIG. 63.

After the “final classifier” of FIG. 63 was defined and constructed, wesubjected the set of samples in the development set to theclassification procedure shown in FIG. 63. Twenty eight samples (20%)were classified as Bad, 61(44%) as Other and 49 (36%) as Good.

The patients' clinical characteristics by classification are shown intable 67.

TABLE 67 Patient characteristics by test classification for classifierrun on all the 138 samples Bad (N = 28) Other (N = 61) Good (N = 49)n(%) n(%) n(%) x² p value Age Median 60 60 56 (Range) (41-78) (18-88)(18-83) FIGO 1  0 (0)  1 (2) 12 (24) <0.001 2  0 (0)  2 (3)  1 (2) (1 +2 vs 3 vs 4) 3 11 (39) 26 (43) 17 (35) 4 13 (46) 13 (21)  3 (6) N/A  4(14) 19 (31) 16 (33) Histology Grade 1  0 (0)  2 (3)  5 (10) 0.113 2 12(43) 20 (33) 21 (43) 3 16 (57) 39 (64) 21 (43) Histology Non-  6 (21) 10(16) 22 (45) 0.003 Serous Serous 22 (79) 51 (84) 27 (55) Residual TumorNo  6 (21) 36 (59) 43 (88) <0.001 Yes 22 (79) 25 (41)  6 (12) MetastaticNo 19 (68) 52 (85) 47 (96) 0.004 Disease Yes  9 (32)  9 (15)  2 (4)“Platinum No  7 (25) 39 (64) 42 (86) <0.001 Resistant” as Yes 11 (39) 14(23)  6 (12) (No vs Yes) assigned by N/A 10 (36)  8 (13)  1 (2)investigator

As a test for platinum resistance as assigned by the investigator,classification Bad compared with Other or Good has 35% sensitivity and92% specificity.

Classification is strongly associated with the known prognostic factorsof FIGO score, histology, presence of metastatic disease and presence ofresidual tumor post-surgery.

FIGS. 64A and 64B show the Kaplan-Meier plots by classification groupfor OS and DFS for the classifications produced by the classifier ofFIG. 63. The associated survival analysis statistics are given in tables68 and 69. Note the extremely poor outcomes, particularly DFS, for thegroup assigned the label Bad, and the particularly good outcomes for thegroup assigned the label Good.

TABLE 68 Medians for time-to-event endpoints by classification groupMedian OS (95% Median DFS (95% CI) in months CI) in months Bad 12 (5-23) 7 (3-14) Other 39 (28-50) 20 (14-29) Good Not reached (51- Not reached(48- undefined) undefined)

TABLE 69 Survival analysis statistics between classification groups OSDFS log-rank p CPH p HR (95% CI) log-rank p CPH p HR (95% CI) Bad vsGood <0.001 <0.001 0.13 <0.001 <0.001 0.10 (0.06-0.26) (0.05-0.22) Badvs Other <0.001 <0.001 0.31 <0.001 <0.001 0.28 (0.18-0.53) (0.16-0.49)Other vs Good <0.001 <0.001 0.34 <0.001 <0.001 0.35 (0.18-0.64)(0.19-0.62)These results indicate that our hierarchical classifier shown in FIG. 63is able to stratify the patients into three groups with better, worse,and intermediate outcomes. As can be seen from the data in tables 70 and71, patients with samples classified as Good are likely to have goodlong term outcomes on platinum-based chemotherapy, while patients withsamples classified as Bad are very unlikely to have good long termoutcomes on platinum-based chemotherapy.

TABLE 70 Proportions still alive and disease-free at key timepoints BadOther Good % alive at 1 year 46 88 96 % alive at 2 years 28 72 89 %disease-free at 6 months 54 90 96 % disease-free at 1 year 35 75 88

TABLE 71 Number of patients disease-free at key timepoints Bad OtherGood # DFS ≦ 4 months (N = 14)  9 (64%)  3 (21%)  2 (14%) # DFS >4months (N = 121) 17 (14%) 57 (47%) 47 (39%) # DFS ≦ 6 months (N = 20) 12(60%)  6 (30%)  2 (10%) # DFS >6 months (N = 115) 14 (12%) 54 (47%) 47(41%) # DFS ≦ 10 months (N = 32) 16 (50%) 11 (34%)  5 (16%) # DFS >10months (N = 100)  9 (9%) 48 (48%) 43 (43%) # DFS ≦ 1 year (N = 38) 17(45%) 15 (39%)  6 (16%) # DFS >1 year (N = 92)  8 (9%) 42 (46%) 42 (46%)In terms of predicting 6 months disease free survival status, aclassification of Bad compared with Other or Good has a sensitivity of60% and specificity of 88% (odds ratio=0.09 Wald 95% CI: 0.03-0.27). Forprediction of 12 months disease free survival status, a classificationof Bad compared with Other or Good has a sensitivity of 45% andspecificity of 91%.

Table 72 shows the multivariate analysis of classification Bad vs NotBad (i.e., Other or Good). This shows that while the classification isstrongly correlated with other prognostic factors (see table 67), itremains a clearly statistically significant predictor of both OS and DFSwhen adjusted for other known prognostic factors. This indicates thatthe classification can provide additional information to otherprognostic factors available to physicians.

TABLE 72 Multivariate analysis of OS and DFS OS DFS P P Covariate HR(95% CI) value HR (95% CI) value NotBad (Other or 0.35 (0.20- <0.0010.30 (0.17-0.55) <0.001 Good) vs Bad 0.62) FIGO 1-3 vs 4 0.35(0.19-0.63) <0.001 0.52 (0.29-0.93) 0.027 FIGO NA vs 4 0.47 (0.24-0.88)0.019 0.80 (0.41-1.55) 0.509 Non-Serous vs Serous 0.85 (0.46-1.58) 0.6150.77 (0.43-1.39) 0.386 Tumor Residual (yes 2.25 (1.30-3.90) 0.004 1.81(1.06-3.08) 0.031 vs no)In terms of predicting disease free survival status at six months, theanalysis can be adjusted for possible confounding factors using logisticregression. The results are shown in table 73.

TABLE 73 Adjustment of odds ratio for prediction of DFS at 6 months forpotential confounding factors Covariate Odds Ratio (95% CI) P value(Other or Good) vs Bad 0.18 (0.05-0.65) 0.009 FIGO 1-3 vs 4 0.31(0.08-1.20) 0.089 FIGO NA vs 4 0.26 (0.05-1.40) 0.118 Serous vsNon-Serous 4.36 (1.17-16.17) 0.028 Tumor Residual (yes vs 3.05(0.83-11.25) 0.094 no)Classification (Bad vs Other or Good) remains a significant predictor ofDFS status at 6 months even when adjusted for potential confoundingfactors.

Conclusions from the Ovarian Cancer/Platinum Chemotherapy Classifiers

We were able to construct classifiers that could separate ovarian cancerpatients treated with surgery and platinum based chemotherapy intogroups with better and worse outcomes from mass spectra of pretreatmentserum samples. The classifier constructed using half of the reduced setof 129 sample set for development (Classifier A) validated well on theremainder of the samples held for internal validation, and the resultsfor the cohort as a whole indicated promising performance. While thetest classification was associated with baseline clinical factors knownto have prognostic significance, it still showed a trend to statisticalsignificance for providing additional information for prediction ofoutcomes.

By selecting clinically distinct patient subgroups from the whole cohortto use for classifier development it was possible to construct aclassification system composed of multiple hierarchical classifiers thatcould stratify the ovarian cancer patients into three classes: one withvery good outcomes (“Good”), one with very poor outcomes (“Bad”) and athird with intermediate outcomes (“Other”). This classification was alsostrongly correlated with other prognostic factors, but Bad versus Otheror Good classifications retained its ability to predict outcome withclear statistical significance even when adjusted for other prognosticfactors in multivariate analysis. This indicates that the classificationcould be of direct clinical utility for physicians advising or makingtreatment decisions for patients in this indication, providinginformation supplementary to that available to them from their patients'clinical characteristics.

Interpreted in terms of a test to identify patients who are platinumresistant or platinum refractory, a classification of Bad vs Other orGood showed 60% sensitivity and 88% specificity for identification ofpatients progressing within 6 months of surgery (odds ratio 0.09). Itremained a strong statistically significant predictor of DFS status atsix months when adjusted for potential confounding factors, indicatingthat it again provides physicians with additional information to informpatient care.

To summarize, in this Example we have described a method of generatingan ensemble of classifiers. The method includes the steps of:

a. from a set of patient samples, defining a plurality of classifierdevelopment sample sets, each of which have different clinicalcharacteristics (in this example, different proportions of tumor sizes,but in practice this could be different proportions of any clinicalcharacteristic which might be relevant to classifier performance, suchas age or age group, smoker status, disease stage, level of a serum ortissue protein or gene expression, mutation status, performance status,surgical resection status, menopausal status, number of lines or kindsof prior therapy received, response to prior lines of therapy, histologyclass or grade, etc., tumor size, or other types of groupings of thedevelopment sample set into different clinical groups)

b. conducting mass spectrometry on the set of patient samples andstoring mass spectrometry data (for example using Deep MALDI andgenerating a feature table for the mass spectral features listed inAppendix A; it will be appreciated that Deep MALDI and Appendix Afeatures are not necessary and are offered by way of example and notlimitation)

c. using a programmed computer, conducting a classifier developmentexercise using the mass spectral data for each of the development setsdefined in step a. and storing in a memory associated with the computerthe parameters of the classifiers thus generated (it being understoodthat the procedure of FIG. 8 steps 102-150 is offered by way of exampleand not limitation), thereby generating an ensemble of classifiers;

and

d. defining a rule or set of rules for generating a class label for atest sample subject to classification by the ensemble of classifiersgenerated in step c. For example, the rules could be if all classifiersgenerate the same class label, assigning to the test sample that classlabel, or some new class label such as “Bad” (with all classifiers inthe ensemble assigned the Early class label) or Good (with allclassifiers in the ensemble assigning a Late class label). As anotherexample the rules could be assigning a label to the test sample based ona majority vote of the ensemble of classifiers. As another example,assigning to the test sample a label in accordance with a ternaryclassification scheme, in which a class label of “other” or theequivalent is assigned if the class labels produced by the ensemble ofclassifiers for the test sample are not all the same, and if allclassifiers in the ensemble generate the same class label assigning thesample a class label indicative of such unanimity of classifications,such as Good or Bad as in Example 5.

As another example, a method of testing a sample using an ensemble ofclassifiers is contemplated, wherein the ensemble of classifiers aregenerated using steps a., b., c., and d., mass spectral data of the testsample is classified by each of the members in the ensemble, and a classlabel is assigned to the test sample according to the rule or set ofrules.

The following clauses are offered as further descriptions of theinvention disclosed in Example 9:

1. A method of generating an ensemble of classifiers from a set ofpatient samples, comprising the steps of:

a. defining a plurality of classifier development sample sets from theset of patient samples, each of which have different clinicalcharacteristics;

b. conducting mass spectrometry on the set of patient samples andstoring mass spectrometry data;

c. using a programmed computer, conducting a classifier developmentexercise using the mass spectral data for each of the development setsdefined in step a. and storing in a memory associated with the computerparameters defining the classifiers thus generated, thereby generatingan ensemble of classifiers, one for each classifier development sampleset;

and

d. defining a rule or set of rules for generating a class label for atest sample subject to classification by the ensemble of classifiersgenerated in step c.

2. The method of clause 1, wherein the patient samples are samples fromcancer patients, and wherein each of the development sets have differentproportions of patients having a given clinical characteristic.3. The method of clause 2, wherein the clinical characteristic is tumorsize.4. The method of clause 3, wherein step c. comprises performing theprocedure of FIG. 8 steps 102-150.5. The method of clause 1, wherein the clinical characteristic is atleast one of age or age group, smoker status, disease stage, level of aserum or tissue protein or gene expression, mutation status, performancestatus, surgical resection status, menopausal status, number of lines orkinds of prior therapy received, response to prior lines of therapy,tumor size, and histology class or grade.6. A method of testing a blood-based sample, comprising the steps of:

generating an ensemble of classifiers by performing the method of clause1 on a development set of blood-based samples;

conducting mass spectrometry on the blood-based sample and obtainingmass spectral data, and

classifying the mass spectral data of the blood-based sample with eachof the members of the ensemble and assigning a class label to the testsample according to the rule or set of rules.

7. A multi-stage classifier comprising:

a programmed computer implementing a hierarchical classificationprocedure operating on mass spectral data of a test sample stored inmemory and making use of a reference set of class-labeled mass spectraldata stored in the memory;

wherein the classification procedure further comprises:

a first stage classifier for stratifying the test mass spectral datainto either an Early or Late group or the equivalent;

a second stage classifier for further stratifying the Early group of thefirst stage classifier into Early and Late groups (or Earlier and Latergroups, or the equivalent), the second stage classifier operating on themass spectral data of the test sample if the first stage classifierclassifies the test mass spectral data into the Early group and whereinthe Early or Earlier class label, or the equivalent, produced by thesecond stage classifier is associated with an exceptionally poorprognosis; and

a third stage classifier for further stratifying the Late group of thefirst stage classifier into Early and Late groups (or Earlier and Latergroups, or the equivalent), the third stage classifier operating on themass spectral data of the test sample if the first stage classifierclassifies the test mass spectral data into the Late group, wherein aLate or Later class label, or the equivalent, produced by the thirdstage classifier is associated with an exceptionally good prognosis.

8. The multi-stage classifier of clause 7, wherein the third stageclassifier comprises one or more classifiers developed from one or moredifferent clinical sub-groups of a classifier development set used togenerate the first level classifier.9. A method of generating a classifier for classifying a test sample,comprising the steps of:

(a) generating a first classifier from measurement data of a developmentset of samples using a classifier development process;

(b) performing a classification of the measurement data of thedevelopment set of samples using the first classifier, thereby assigningeach member of the development set of samples with a class label in abinary classification scheme (Early/Late, or the equivalent);

(c) generating a second classifier using the classifier developmentprocess with an input classifier development set being the members ofthe development set assigned one of the two class labels in the binaryclassification scheme by the first classifier, the second classifierthereby stratifying the members of the input classifier development setwith the first class label into two further sub-groups.

10. The method of clause 9, further comprising the steps of:

(d) dividing the development set of samples into different clinicalsubgroups 1 . . . N where N is an integer of at least 2;

(e) repeating the classifier development process for each of thedifferent clinical subgroups 1 . . . N, thereby generating differentthird classifiers C1 . . . CN; and

(f) defining a hierarchical classification process whereby:

i. a patient sample is classified first by the first classifiergenerated in step a);

if the class label assigned by the first classifier is the class labelused to generate the second classifiers, then classifying the patientsample with the second classifier; and

iii. if the class label assigned by the first classifier is not theclass label used to generate the second classifier, then classifying thepatient sample with the third classifiers C1 . . . CN; and

iv. generating a final label as a result of classification steps ii orstep iii.

11. A classifier generation method, comprising:

a) obtaining physical measurement data from a development set of samplesand supplying the measurement data to a general purpose computer, eachof the samples further associated with clinical data;

b) generating a first classifier (Classifier A) from the measurementdata of the development set of samples;

c) identifying a plurality of different clinical sub-groups C1 . . . CNwithin the development set based on the clinical data;

d) for each of the different clinical sub-groups, conducting aclassifier generation process from the measurement data for each of themembers of the development set that is associated with such clinicalsub-groups thereby generating clinical subgroup classifiers C1 . . . CN;

e) storing in memory of a computer a classification procedure involvingthe Classifier A and the classifiers C1 . . . CN generated in step d).

12. The method of clause 11, wherein the classifier development of stepsb) and d) is in accordance with the procedure of FIG. 8 steps 102-150.13. The method of clause 11 or clause 12, wherein the method furthercomprises a step of conducting a bagged filtering operation to filterthe measurement data obtained from the samples to either deselect junkyfeatures in the measurement data or select a subset of the features inthe measurement data which have significant classification performance.14. The method of clause 13, wherein the classifier generation processis performed iteratively with the bagged filtering operation to deselectjunky features or select a subset of features which have significantclassification performance.15. The method of any of clauses 11-14, wherein the measurement datacomprises MALDI-TOF mass spectrometry data.16. The method of any of clause 15, wherein the MALDI-TOF massspectrometry data is acquired from a process in which each of thesamples in the development set is subject to at least 100,000 lasershots.17. A method comprising generating an ensemble of classifiers each basedon different proportions of patients with large and small tumors andgenerated in a computer from mass spectrometry data of blood-basedsamples from a development set of samples, and defining a classificationprocedure using the ensemble of classifiers.18. The method of clause 17, wherein the ensemble of classifierscomprises the ensemble of classifiers identified as IS6 in thisdocument.19. A method of guiding treatment of a melanoma patient comprisingperforming mass spectrometry on a blood-based sample from the patientand generating a class label of a blood-based sample using an ensembleof classifiers generated in accordance with clause 17, and using theclass label to guide the patient in treatment of the melanoma.20. The method of clause 19, wherein the treatment comprisesadministration of nivolumab.21. The method of clause 19, wherein the guiding of treatment comprisesnot administering the combination of nivolumab and ipilimumab.

Example 10 Method and System for Measurement of Biological FunctionScores Using Mass Spectrometry Data and Uses Thereof, Including GuidingTreatment, Predicting Survival, and Developing Classifiers

This Example details the methodology and systems which are used in orderto obtain a novel and useful score (e.g., a numerical value) thatmeasures a particular biological function, e.g. acute response, woundhealing, complement system, or other, for a given patient. Such abiological function score is calculated from mass spectral data from aserum sample. By way of example and not limitation, in this Example weobtain mass spectral data using the Deep MALDI technique as describedpreviously in this document from Example 1.

Data used in Example 10

In order to obtain the results presented in Example 10, the followingfive sample sets were used:

“Analysis” set—composed of 49 patients, most with non-small cell lungcancer (NSCLC), but a few with chronic obstructive pulmonary disease(COPD) but no cancer. Matched mass spectral data (298 features) andprotein expression of 1129 proteins/peptides obtained from running theSomaLogic 1129 panel (see Example 6) were available for these samples.

“PROSE-erlotinib” set—85 patients from the PROSE trial treated witherlotinib. All patients had advanced, previously treated NSCLC. Massspectral data (298 features) and the corresponding classification labelsproduced by the Example 1 “IS2” classifier were available frompre-treatment samples.

“PROSE-chemo” set—123 patients from the PROSE trial treated with singleagent chemotherapy. All patients had advanced, previously treated NSCLC.Mass spectral data (298 features) and the corresponding class labelsproduced by the Example 1 “IS2” classifier were available frompre-treatment samples.

“Moffitt” set—119 melanoma patients treated with nivolumab (anti-PD1agent) at Moffitt Cancer Center, see Example 1. The samples werecollected before treatment. Mass spectral data (298 features) and thecorresponding classification labels produced by the Example 1 “IS2”classifier were available.

“Moffitt-Week7” set—a subset of 107 patients from the “Moffitt” cohort,collected 7 weeks after beginning of treatment. See Example 7. Massspectral data (298 features) and the corresponding classification labelsproduced by the Example 1 “IS2” classifier were available.

Sample Preparation and Pre-Processing

Samples were prepared and mass spectra acquired using standard DeepMALDI acquisition procedures explained in Example 1. We improved a fewof the processing parameters, but the details are not particularlyimportant and do not make any principal difference to the presentresults. For example, we used slightly different feature definitionsthan were used in Example 6 and excluded a few features that have shownsome reproducibility issues. Consequently, the number of featuresassociated with a particular protein set or functional group for a fixedp value may vary from what was specified in Example 6 and used forclassifier development in Example 6.

PSEA Protein Set Enrichment Analysis

The correlation of each mass spectral (MS) feature with the biologicalfunctions described in this Example was calculated by running ProteinSet Enrichment Analysis—PSEA (a variant of the Gene Set EnrichmentAnalysis method, applied to protein expression data) on the “Analysis”set. The PSEA correlates MS features with expression of multipleproteins, rather than expression of single proteins, providing someprotection against identifying randomly correlated and not generalizableassociated features. This method also allows for the identification of asignificant effect that is smaller in magnitude (per protein) than thatwhich could be identified in a univariate analysis. The protein setswere created based on the intersection of the list of SomaLogic 1129panel targets and results of queries from GeneOntology/AmiGO2 andUniProt databases. The PSEA returns, for each MS feature and protein setpair, an enrichment score, ES, which reflects the degree of correlation,and a p-value that reflects the significance of the ES when comparedwith the null distribution of no correlation. For further details, seeExample 6 and the Mootha et al. and Subramanian et al. papers cited inExample 6.

Biological Function Score—Methodology

Given a biological function, e.g., Wound Healing or Acute Response, wedetermined which MS features were correlated with the correspondingprotein set at the α=0.05 significance level (unadjusted for multiplecomparisons) using the PSEA results of the “Analysis” set. A PrincipalComponent Analysis (PCA) is then performed using the N_(S)=85 samplesfrom the “PROSE-erlotinib” set. PCA is a known methodology in statisticsand data analysis to reduce a complex data set to lower dimensions andreveal hidden, simplified dynamics underlying it. As shown in FIG. 81,the procedure for calculation of a biological function score is shown at8100 in a flow-chart form. It will be appreciated that the flow chart ofFIG. 81 and the description below can be reduced to a set of programmedinstructions by those skilled in the art. At step 8102, a particularfeature table for a sample set (ss) is computed set, F^(SS). At step8104, we perform a PCA over many realizations of subsets of thedevelopment sample set. At step 8106 we compute a PCA bagging procedureand arrive at a first principal component û₁. At step 8108 we compute abiological function score b^(ss)=F^(ss)·û₁ for each member of the sampleset. At step 8110 we calculate the biological function score of othersample sets, if present. Since the number of samples available for thecalculations was small and thus prone to leading to a randomly biasedPCA solution, we implemented a bagged version of PCA step 8106. Theprocedure in more detail is as follows:

-   -   1. Step 8102 Construct partial feature table for a sample set,        here the “PROSE-erlotinib” set        -   a. For each sample in the “PROSE-erlotinib” set, the subset            of MS features significantly correlated with the biological            function (as determined using the PSEA of the “Analysis”            set), was extracted from the total of 298 available            features. This resulted in a “partial feature table”:            F^(erlotinib)=f_(si) with 1≦i≦N_(f) (N_(f) is the number of            significantly correlated MS features) and 1≦s≦N_(S)            (N_(S)=85 samples) runs over the sample indices. FIG. 82 is            an illustration of the partial feature table matrix F^(ss)            for a given sample set (ss), such as the PRO SE-erlotinib            sample set.    -   2. Step 8104 Perform PCA over many subset realizations of the        sample set        -   a. A subset of N_(S′)=56 samples was randomly chosen (using            Matlab® R2015a randperm function) out of the 85 available.            (Note that the choice of a subset of 56 of the 85 samples is            arbitrary. It was chosen to be approximately ⅔ of the            cohort, which is a good trade-off between sufficient samples            in the subset, and diversity of the subset realizations.)        -   b. The PCA was implemented using the Matlab® R2015a pca            function, which returns a matrix containing the principal            component coefficients, C, of dimensions N_(f)×N_(f) if            N_(f)<N_(S′), or N_(f)×(N_(S′)−1) if N_(f)≧N_(S′). This            matrix allows the transformation of a data point (sample)            represented in the MS feature space into an hyper-space            whose basis vectors (columns of C=[u₁ . . . u_(N) _(f) ], or            C=[u₁ . . . u_(N) _(S′) ⁻¹]) define directions of decreasing            variance in the data. The pca function also returns a list            of percentages of the total data variance explained by each            principal component. It was found that, for the studied            protein sets (biological functions) presented here, the            first principal component explained the majority (65% or            more) while the second principal component explained less            than 15%) of the variance (see FIGS. 65A-65C). Therefore, we            considered only the first Principal Component (PC),

${u_{1} = \begin{bmatrix}u_{11} \\\ldots \\u_{1i} \\\ldots \\u_{1N_{f}}\end{bmatrix}},$

-   -   -    for the calculation of the biological function score,            corresponding to the first column of C. Note that u₁ is a            vector with scalar values of the first PC for each of the            N_(f) features.        -   c. Steps 1a. and 1b. were repeated N_(r)=2¹⁷=131,072 times,            drawing a different subset of 56 samples from the            “PROSE-erlotinib” set at each iteration.

    -   3. Step 8106 PCA bagging        -   a. A total of 2¹⁷ first principal components had been            calculated at this point. Subsets of 2 first principal            components were taken, each pair was then averaged and the            resulting average normalized according to the following            calculation:

$\begin{matrix} u_{1i}^{k}arrow{{\frac{u_{1i}^{k} + u_{1i}^{k + {N_{r}/2}}}{\sqrt{\sum_{l = 1}^{l = N_{f}}( {u_{1l}^{k} + u_{1l}^{k + {N_{r}/2}}} )^{2}}}\mspace{14mu} {with}\mspace{14mu} 1} \leq k \leq 2^{16}}  & {{Equation}\mspace{14mu} (2)}\end{matrix}$

-   -   -   b. Step a. was repeated 16 more times, obtaining in each            iteration half as many averaged and normalized first            principal components as the previous iteration, until one            final bagged first principal component, û₁, was obtained. û₁            (first principal component) is a vector with entries for            each of the mass spectral features. More particularly, û₁ is            just the average of u₁ over the sample set realizations. We            bag the PCA to give a more robust estimator of the first            principal component vector.

FIG. 83 is an illustration of the final bagged (or average) firstprincipal component û₁.

-   -   4. Step 8108 Biological function score calculation        -   a. A vector of the biological function scores for the            “PROSE-erlotinib” samples, b^(erlotinib), was then            calculated, which consisted of the projection of the sample            MS feature vectors onto the direction of the first principal            component û₁:

$\begin{matrix}{b^{erlotinib} = {\begin{bmatrix}b_{1} \\\ldots \\b_{s} \\\ldots \\b_{N_{s}}\end{bmatrix} = {F^{erlontinib} \cdot {\hat{u}}_{1}}}} & {{Equation}\mspace{14mu} (3)}\end{matrix}$

The scalar numbers b₁, b₂, . . . b_(s), . . . b_(N) _(S) are thebiological function scores for samples 1, 2, . . . s, . . . Ns,respectively, in the sample set. In Equation (3) the first element of û₁gets multiplied by the first feature to give its contribution to thescore, the second element of û₁ gets multiplied by the second feature togive its contribution to the score, etc. for all features and these aresummed up to give the final score. Or put another way, the score for agiven sample is the projection of the vector of feature values for thatsample onto the first principal component vector û₁. As will be shown inthe following Results section, the biological function score is a numbertypically between −5 and +50. While the magnitude of the number isimportant and can give insight to a given biological function associatedwith the score, especially if it is obtained over a period of time froma series of samples, of perhaps greater importance is its value relativeto samples from other patients in a suitable population of patients,e.g., melanoma or NSCLC patients. The meaning and use of the scores willbe explained in later sections of Example 10.

The process of FIG. 81 can be performed many times, e.g., when onewishes to obtain biological functions scores for different biologicalfunctions in the development set of samples.

-   -   5. Step 8110 Calculation of the biological score for the other        sample sets        -   a. Similarly to step 4, the biological function scores were            calculated for the other four sample sets of this example            using the averaged first principal component û₁ determined            in the “PROSE-erlotinib” set (step 3)

$b^{{sample}\text{-}{set}} = {\begin{bmatrix}b_{1} \\\ldots \\b_{s} \\\ldots \\b_{N_{s}^{{sample}\text{-}{set}}}\end{bmatrix} = {F^{{sample}\text{-}{set}} \cdot {\hat{u}}_{1}}}$

Example 10 Results

Acute Response Score

Twenty-nine MS features were determined to be correlated by PSEA withthe protein set corresponding to acute response (AR) and were used inthe calculations of the corresponding Acute Response Score. FIG. 66A-66Dshows the distributions of the Acute Response Score (AR score) in the“PROSE-erlotinib”, “PROSE-chemo”, “Moffitt” and “Moffitt-Week7” samplesets, respectively. Notably, the distributions of the Acute ResponseScores between values of −2 and +4 are quite similar across all samplesets, even between NSCLC and melanoma sample sets.

Kaplan-Meier plots of the Overall Survival (OS) and Time to Progression(TTP) for all the 119 patients of the “Moffitt” set were already shownin FIGS. 1A and 1B. A Cox model applied to these time-to-event datausing the AR Score as the single explanatory variable yields thestatistics presented in Table 74. Table 75 shows the same statisticswhen a multivariate analysis is considered including known baselineprognostic factors.

Table 74: Statistics obtained by applying a Cox model to thetime-to-event data of the “Moffitt” set using AR score as the singleexplanatory variable

TABLE 74 Statistics obtained by applying a Cox model to thetime-to-event data of the “Moffitt” set using AR score as the singleexplanatory variable OS TTP HR p- HR p- (95% CI) value (95% CI) value AR1.58 <0.001 1.51 <0.001 score (1.27-1.97) (1.24-1.85)

TABLE 75 Statistics obtained from a multivariate analysis of the“Moffitt” set OS TTP HR (95% CI) p-value HR (95% CI) p-value AR score1.59 (1.23-2.04) <0.001 1.57 (1.24-2.00) <0.001 Female vs Male 0.55(0.31-0.97) 0.038 0.56 (0.35-0.90) 0.017 PD-L1 (5%)-vs+ 0.53 (0.18-1.62)0.267 0.75 (0.29-1.92) 0.542 PD-L1 (5%)-vs 0.78 (0.42-1.42) 0.415 1.00(0.58-1.74) 0.988 NA Prior Ipi (no vs 0.67 (0.38-1.19) 0.169 0.74(0.45-1.22) 0.242 yes) LDH/1000 1.58 (1.11-2.26) 0.011 1.47 (1.07-2.04)0.019The AR score was defined without use of outcome data. On an independentsample set it is a significant predictor of both OS and TTP, and itremains a significant independent predictor of OS and TTP when adjustedfor other known prognostic factors.

FIGS. 67A-67D show the distributions of the AR score in the“PROSE-erlotinib”, “PROSE-chemo”, “Moffitt” and “Moffitt-Week7” samplesets, split by IS2 classification label. (Recall that “IS2” refers tothe “full set” classifier developed in Example 1 on the Moffittnivolumab sample set). We performed a t-test as well as Mann-Whitneytest to investigate the association of the AR scores with the IS2classification groups and obtained p-values <0.001 for all the samplesets. Based on the distributions of the score for IS2 Early and IS2 Latesamples in the “PROSE-chemo” data set we chose a tentative threshold (ARscore) of −1.25 in order to define samples that had higher and lower ARfunction, according to their score being higher or smaller than thethreshold, respectively. FIGS. 68A-68F show the Kaplan-Meier plots fortime-to-event outcomes (OS and progression-free survival (PFS) or TTP)of the “PROSE-chemo”, “PRO SE-erlotinib” and “Moffitt” sets by groups asdefined by the chosen threshold. Note that there is a separation in thesurvival plots of FIG. 68A-68F, namely the group of patients with ascore of >−1.25 had a relatively worse OS and PFS as compared to thegroup of patients with a score of <−1.25. The statistics of the survivalplots are shown in the legends for FIGS. 68A-68F.

FIGS. 68A-69F thus shows that it is possible to use the AR Scoretogether with a cutoff to stratify patients with both melanoma and NSCLCinto two groups with better and worse time-to-event outcomes.

FIGS. 69A-69D shows the evolution of the Acute Response Score for the107 patients with samples both in the “Moffitt” and the “Moffitt-Week7”sets, grouped by combination of IS2 label at baseline (before treatment)and week 7 (during treatment). FIGS. 70A-70C shows the evolution of theacute response score for the 107 patients with samples both in the“Moffitt” and the “Moffitt-Week7” sets, grouped by treatment response.As expected from the plots of FIG. 69A-69D, patients with an “Early” IS2classification generally have higher AR scores. Changes of IS2 labelfrom “Early” to “Late” are associated with a lowering of the AR scoreand vice versa for changes from “Late” to “Early”. Recall from Example 1that class label “Late” indicates that the patient is a member of aclass of patients that are likely to obtain relatively greater benefitfrom nivolumab in treatment of melanoma as compared to patients that area member of the class of patients having the class label “Early.” Theselongitudinal assessments show that a patient's AR score can changeduring the course of therapy. Hence, it is possible, by collecting aseries of serum samples and evaluating the AR score, to monitor thelevel of acute response in a cancer patient.

To explore the value of monitoring of AR score further we investigatedthe prognostic impact of changes in the score. Change in AR scorebetween week 7 and baseline was an independent significant predictor ofOS and TTP for the “Moffitt” set in addition to baseline AR score (Table76). So, monitoring the AR score of melanoma patients treated withnivolumab provides additional information to a baseline assessment of ARscore.

TABLE 76 Statistics obtained by applying a Cox model to thetime-to-event data of the “Moffitt” set using AR score and change in ARscore between baseline and week 7 as explanatory variables OS TTPP-value HR 95% CI P-value HR 95% CI Baseline AR 0.007 1.50  1.12-2.00<0.001 1.77 1.36-2.30 score Change in 0.013 0.69 10.52-0.93 <0.001 0.580.45-0.75 AR scoreTo illustrate the prognostic value of monitoring of AR score further,the Kaplan-Meier plots for the 107 patients in the “Moffitt” set withsamples at both baseline and week 7 are shown in FIGS. 71A and 71B withthe patients grouped according to change in AR score from baseline toweek 7. Patients with an increase of the AR score in the course oftreatment have significantly shorter TTP and OS than the rest of thepatients, which is in agreement with the poor prognostic value of thehigh baseline AR score.

Wound Healing Score

Twenty-five MS features were determined to be correlated with theprotein set corresponding to wound healing (WH) but not correlated witheither acute response or immune response. Those features were used inthe calculations of a Wound Healing Score in accordance with thebiological function score calculation procedure explained above. FIGS.72A-72D show the distributions of the scores in the “PROSE-erlotinib”,“PROSE-chemo”, “Moffitt” and “Moffitt-Week7” sample sets, respectively.Note in the plots the legend for Wound Healing uses the abbreviationWH-AR-IR, meaning that the feature set contains features associated withwound healing but not correlated to either acute response (AR) or immuneresponse (IR). To arrive at this reduced set of features we did not justto look at all MS features associated with wound healing with p<0.05,but rather we identified those features associated with wound healing atp<0.05, excluding those that are associated with acute response orimmune response with p<0.05, hence the terminology WH-AR-IR. If we donot exclude the features associated with AR and IR (which also overlap),we would (likely) get similar behavior for the AR and WH scores, becausethe AR features tend to dominate the behavior. What would be ideal wouldbe to have a bigger, i.e., more complete, protein panel run on a muchlarger set of samples; then we could use much more refined, less broadbiological functions to start with and still have enough measuredproteins in each set for a meaningful analysis. As it stands the proteingroups we have are broad and tend to overlap. In the followingdiscussion, the term Wound Healing and Wound Healing Score means the setof mass spectral features (and associated Score) which is associatedwith the wound healing biological function but not significantlycorrelated with either AR or IR.

FIGS. 73A-73D show the evolution of the Wound Healing Score plotted onthe Y axis (“WH-AR-IR” in the figures) for the 107 patients with samplesboth in the “Moffitt” and the “Moffitt-Week7” sets, grouped bycombination of IS2 label at baseline (before treatment) and week 7(during treatment). FIGS. 74A-74C show the evolution of the woundhealing score for the same 107 patients, grouped by treatment response,namely progressive disease (FIG. 74A), partial response (FIG. 74B) andstable disease (FIG. 74C).

As illustrated in FIGS. 72A-72D, the Wound Healing Score appears to havea somewhat different distribution depending on tumor type, with thedistributions for melanoma (Moffitt plots FIGS. 72C and 72D) centered ata lower WH score than those for NSCLC (PROSE plots 72A and 72B). Theassociation of IS2 classification with WH score is markedly less strongthan it is with AR score. However, Cox proportional hazard models of OSand TTP for the “Moffitt” set show that WH score is a highly significantpredictor of outcome (Table 77). In addition, inclusion in the Coxmodels of change of WH score from baseline to week 7 as an additionalexplanatory variable show that this is independently significant, sothat monitoring of WH score during treatment provides additionalprognostic information.

TABLE 77 Cox proportional hazard analysis of OS and TTP for the“Moffitt” set with WH score as the single explanatory variable (Model 1)and with baseline WH score and change in WH score from baseline to week7 as simultaneous explanatory variables (Model 2) OS TTP P-value HR 95%CI P-value HR 95% CI Model 1 WH alone <.001 0.92 0.88-0.96 <.001 0.930.90-0.96 Model 2 Baseline WH 0.001 0.91 0.87-0.96 <0.001 0.91 0.87-0.96score Change in WH 0.037 1.05 1.00-1.09 0.003 1.06 1.02-1.10 score

Complement System Score

One hundred fifty-seven (157) MS features were determined to becorrelated with the protein set corresponding to the complement system(see Example 6). Those features were used in the calculations of thecomplement system score using the procedure explained above includingEquations (2) and (3). FIGS. 75A-75D show the distributions of the scorein the “PROSE-erlotinib”, “PROSE-chemo”, “Moffitt” and “Moffitt-Week7”sample sets. FIGS. 76A-76D show the evolution of the complement systemscore for the 107 patients with samples both in the “Moffitt” and the“Moffitt-Week7” sets, grouped by combination of IS2 label at baseline(before treatment) and week 7 (during treatment). FIGS. 77A-77D show theevolution of the complement system score for the same 107 patients,grouped by treatment response, namely progressive disease (FIG. 77A),partial response (FIG. 77B) and stable disease (FIG. 77C).

FIGS. 75A-75D indicate again that there is some difference in thelocation of the distributions of the complement system score dependingon tumor type, with NSCLC being centered at higher levels of complementscore as compared to melanoma. IS2 classification (Example 1) of Late isgenerally associated with somewhat higher levels of complement scorecompared with IS2 classification of Early, and changes of classificationfrom Early to Late show a general decrease in complement score, and viceversa for changes from Late to Early.

Note that as the first principal component is defined only up a factorof multiplication by −1, and generally protein sets associated with agiven biological function will contain proteins that have both higherlevels and lower levels when this biological function is more relevantor more active, it is not obvious from inspecting the score whether ahigh score or a low score corresponds to more relevant or activebiological function. In our PSEA analysis of our IS2 classifications(Example 6), we observed that complement and acute response were bothelevated in the IS2 Early classification group compared with IS2 Lateclassification group. This observation is consistent with the resultspresented here in Example 10, in that the complement score should beinterpreted so that the elevated levels of features (and tentativelyidentified corresponding proteins (Example 1 Protein Identification)correspond to lower levels of this complement score.

Uses of the Biological Function Scores for Treatment and Monitoring

Summarizing the results presented above, we envision severalapplications of biological function scores, both in relation to theexisting classification labels or in the absence of classificationresults. For further discussion, we will use scores associated withAcute Response (AR) function, but the suggestions are applicable to anybiological function that can be associated with mass spectral features.

1. Biological Function Scores in Relation to Existing Classifications

In the first case, using the classification labels, e.g. Early and Late,obtained by IS2 (Example 1 full set classifier), we can evaluate whetherthere is a significant difference between the distributions of scorevalues in the two groups (FIG. 68A-68D). In all our studied examples thedistribution of AR scores were highly significantly different (p<0.001),as assessed both by t-test and Mann-Whitney test, between IS2classifications.

Evidence for this difference may serve as an additional support of theeffect of the biological function associated with the score, on theclassification.

Analyzing the distribution of the score in the groups defined byclassification, we could choose cut-offs that can be used to assign apatient to a specific sub-group, e.g. “high”, “medium”, and “low”, whichcan be correlated with outcome, or prognosis, or some other clinicallyrelevant measure.

For example, a cut-off chosen based on the AR scores of the PROSEchemotherapy NSCLC set (−1.25), based on IS2 classification, separatespatients in this set, as well as in the Moffitt set (which consists ofthe melanoma patients treated with nivolumab) in two groups withsignificantly different OS and PFS (or TTP, in case of Moffitt) (seeFIG. 69). In other words, by assigning a patient sample a biologicalfunction score using the procedure described in this Example andcomparing the score to a cutoff (e.g., defined from scores obtained froma development set of samples) we can assign a class label to the sampleand make a prediction on their response or survival by comparison of thescore to the scores of the group of patients with similar scoresrelative to the threshold and their survival or responsecharacteristics.

2. Biological Function Scores Independent of Other Classifications

Importantly, biological function scores can be used and analyzedindependently of any classification labels.

Thus, the significance of the score, used as an explanatory variable,for outcomes can be evaluated using Cox Proportional Hazards models,either in a univariate or in a multivariate analysis, taking intoaccount additional clinical information (see Tables 74 and 75previously). When used for the Moffitt set, the AR score was a highlysignificant predictor of OS and TTP both in univariate and multivariateanalyses.

Additionally, in the multivariate approach, the effect of severalbiological functions, based on their scores, can be assessed forsignificance of their simultaneous impact on outcomes, which potentiallycan be hypothesis generating for the relative roles of differentbiological functions on outcomes.

The scores associated with a biological function can be used to classifypatients, e.g. using cut-offs that can be defined based on quantiles ofscores in the training set, and then applied to new samples. In thefollowing example we defined the cut-off based on grouping the lower twotertiles of scores associated with the PROSE chemotherapy subset(defined as AR score Low, ≦−0.744), and comparing with the uppertertile >−0.744 (defined as AR score High), and applied these thresholdsto the Moffitt data. The results can be seen in FIG. 78A-78D. Thesefigures are Kaplan-Meier plots for OS, PFS (“PROSE-chemo”) and TTP(“Moffitt” set) by group defined according to the AR score thresholddefined by tertiles in the PROSE set. The corresponding number ofsamples in each group, hazard ratios (HRs), log-rank p-values andmedians are shown below each plot. Note that the patients with the ARLow score have greater OS and PFS as compared with those patients withthe AR High score.

In this example the prognostic classifier defined using data from NSCLCpatients by the AR biological score is shown to significantly separatepatients by OS and TTP in the independent cohort of melanoma patientstreated with nivolumab, see FIGS. 78C-78D.

Alternatively, scores for several biological functions can be used tocreate a more sophisticated classifier, e.g. using the Diagnostic Cortexapproach described in FIG. 8. As an example, we used the three scoresdefined above (Acute Response, Wound Healing and Complement System) asfeatures for classifier development using the Moffitt set with theDiagnostic Cortex, instead of mass spectral features as in previousexamples of FIG. 8. For creation of the mini-classifiers, we used allpossible combinations of one, two, and three features (Scores) to give 7possible mini-classifiers (FIG. 8A, step 120). We used mini-classifierfiltering (of the 7 possible mini-classifiers, FIG. 8A step 126), but wedid not do any feature deselection in further iterations of the loop 135as we did in the similar procedure of FIG. 54A, step 52. So, all 7mini-classifiers were considered for filtering, but not all passed ineach teration of loop 335 and so we did not use all 7 mini-classifiersin the logistic regression forming each master classifier. In fact, fora few test/training split realizations none of the 7 possiblemini-classifiers passed filtering and then this realization was droppedand not used at all.

When the classifier was developed in accordance with FIG. 8 and appliedto the Moffitt set the approach produced two classification groups,“Early” (poor outcomes, Early progression) and “Late” (good outcomes,Late progression). The Kaplan-Meier plots for these classificationgroups are shown in FIGS. 79A and 79B, FIG. 79A showing the overallsurvival by classification group and FIG. 79B showing the time toprogression by classification group. There is excellent stratificationof the patients into groups with better and worse outcomes from theclassifier developed using the three biological functions scores as theonly features.

It would also be possible to relate an individual patient's score to thedistribution of scores obtained for the population of patients in thesame indication, to get an indication of whether the individual patienthas a particularly high or low level of activation of the biologicalfunction being considered. For example, patients with melanoma with anAR score of greater than 1 lie above the 90th percentile of AR score intheir indication, indicating exceptionally high levels of acuteresponse. This could potentially be used to select patients for orindicate against certain therapies. See G Simpson, S D Heys, P HWhiting, et al., Acute phase proteins and recombinant IL-2 therapy:prediction of response and survival in patients with colorectal cancer.Clin Exp Immunol 1995; 99: 143-147.

Accordingly, in one aspect of this disclosure we develop/train aclassifier from a set of biological function scores on a development setof samples, and can use such a classifier and associated reference set(class labels and scores) to classify a new sample. The new sample issubject to mass spectrometry, the feature values are obtained for thefeatures in the set associated with the PCA first principal componentvector for each of the biological functions, and a set of biologicalfunction scores are assigned to the sample using the same procedures togenerate the biological function scores in the development sample set.Then the sample is classified with the classifier and a label isgenerated, e.g., Early or Late or the equivalent. The class label isthen useful for guiding treatment or predicting patient response orsurvival.

3. Using Biological Scores for Patient Monitoring

As shown for example in FIGS. 70A-70D, numerical changes in the score ofa patient in the course of treatment in some cases can be associatedwith the change of classification: thus, decrease of the AR score seemsto be associated with changes of classification from Early to Late byweek 7, while changes from Late to Early seem to correspond with theincrease of the score.

It would be possible to observe changes over time in score, or thechange in percentile of the observed score in the distribution of scoresfor the particular indication to monitor changes in levels of aparticular biological function. This could be used to investigate theeffectiveness of a therapy or to test whether a patient's status haschanged to allow initiation of a therapy. For example, if a therapy isknown to be ineffective when acute response levels are high, a patientmay need to wait until his AR score is reduced, either naturally or bysome intervention, until he should commence that particular therapy.This approach could also be used to monitor chronic diseases to try topredict disease flares or progression.

In addition we have demonstrated (see for example FIGS. 71A-B and Table76) that as well as baseline evaluation of scores, changes in score canbe significant prognostic factors, providing additional information tophysicians to inform patient prognosis.

4. Identifying Differences in Relative Importance of BiologicalFunctions Across Tumor Types

The results of Example 10 show that, while the distribution of AR scoresis quite similar between melanoma patients and patients with NSCLC, thedistributions of WH-AR-IR score and complement score are different.Hence, examination of the score distributions across differentindications may reveal differences in the relative importance ofdifferent biological functions and related pathways across differenttumor types and could be used, for example, to investigate why sometherapies are more effective in some tumor types than in others.

In summary, biological function scores can be used to characterize therole of a biological function in an existing classifier, as well as toclassify patients independently, with the potential to define groupswith different outcomes and prognoses. It can also be used to monitorchanges in level of biological function that may be useful for assessingcourse of disease, effectiveness of therapy, or when a patient canoptimally commence a treatment.

While the examples here are specific, the methodology used in quitegeneral and can be extended in several ways.

1. Choice of What Features to Use within PCA

In these examples, in the PSEA we used features associated with theprotein sets linked to a particular biological function with a p valueof 0.05. This choice of cutoff in level of association is arbitrary andcan be taken to be larger or smaller, thereby bringing in larger orsmaller sets of features associated with a particular biologicalfunction.

2. Use of Only One Principal Component of PCA

In the examples explored here, the first principal component dominatedthe variance within the datasets. See FIGS. 65A-65C and the previousdiscussion. This might not always be the case and it might be necessaryto extend the approach to characterize the level of a particularbiological function by a set of several scores, rather than a singlescore. Alternatively, it might be necessary to combine the projectionsonto several principal components or their locations in the populationdistributions of these projections to create a single score that moreprecisely reflects the biological function.

3. Alternatives to PCA

Here we used simple PCA on the set of features associated with thebiological function to reduce the feature values to one score. It wouldbe possible to use other methods to do this dimensional reduction, forexample, the technique known as kernel PCA. See for example thedescription of this technique on wikipedia.org and the referencestherein.

FIG. 80 summarizes in block diagram form a system and method forcreation of a biological function score. As indicated at 8000, we have adevelopment set of N samples 1, 2, 3, . . . N e.g., from a population ofpatients enrolled in a clinical trial of a drug or all having a disease,e.g., cancer. The samples in this example are blood based samples, e.g.,serum, obtained for example in advance of treatment. The samples aresubject to mass spectrometry as indicated at 8002. Additionally, thesamples are subject to a protein expression assay in a platform 8004(such as the SOMAscan system of SomaLogic, Inc., Boulder Colo. or theequivalent). Protein expression data of a large set of proteins, ideallyat least 1000 such proteins and mass spectrometry data is provided to acomputer 8006. The computer 8006 performs a protein set enrichmentanalysis (PSEA), including derivation of ES scores and p values whichcorrelate sets of proteins associated with particular biologicalfunctions with mass spectrometry features, using the methodologyexplained in Example 6. From this data, the computer generates abiological function score for the biological function(s) identified fromthe PSEA analysis using the procedure explained above. This can be anarray of scores for each member of the development sample set 8000.Additionally and alternatively, a classifier can be developed using FIG.8 using the scores in the development set as features for classificationas explained above, if clinical data or class labels for the samples arealso available for classifier training. The scores for the set ofsamples, along with class labels for the members of the development set,are stored in memory of the computer 80006, for example in later use inclassification of a sample.

As another example, a sample from a new patient with similarcharacteristics as the development sample set is obtained, massspectrometry is performed on the sample and feature values for thefeatures associated with the biological function(s) are obtained, and ascore is assigned to the sample. The score is then compared with thescores in the sample set 8000, or a threshold derived from the scores inthe development sample set, and the score for the new sample is used toguide treatment or predict patient outcome or prognosis.

As shown in FIG. 80, in one alternative a second set of M samples 1, 2,3 . . . . M 8000A could be obtained and the protein expression assay inthe platform 8004 could be performed on this second set of samples. ThePSEA analysis could be done on this set of protein expression datainstead of the first set of samples. This second set of samples couldalso be subject to mass spectrometry and the processes in the computer8006.

The following clauses are offered as further descriptions of theinventions disclosed in Example 10.

1. A system for characterizing a biological function in a human,comprising:

a mass spectrometer conducting mass spectrometry on a blood-based samplefrom the human; and

a computer operating with programmed instructions for generating abiological function score from a feature table of mass spectral featuresobtained from the mass spectrometry of the blood-based samples which areassociated with the biological function projected onto the direction ofa first principal component vector obtained from the mass spectralfeatures and a sample set in the form of a multitude of otherblood-based samples.

2. The system of clause 1, further comprising a protein expression assaysystem obtaining protein expression data from a large panel of proteinsspanning biological functions of interest for each of the samples in thedevelopment set of samples or alternatively each of the samples in thesecond set of samples; and wherein the computer is operable to perform aprotein set enrichment analysis associating proteins or sets of proteinsfrom the large panel of proteins with the biological function.3. A method of evaluating a set of blood-based samples obtained from apopulation of humans, comprising the steps of:

a) obtaining the set of samples;

b) conducting mass spectrometry on the set of samples and obtaining massspectrometry data including feature values of a set of mass spectrometryfeatures;

c) identifying associations of sets of the mass spectrometry featureswith a biological function; and

d) computing a biological function score for each member in the set ofsamples by projecting a feature table containing feature values of themass spectral features which are associated with the biological functiononto the direction of a first principal component vector obtained fromthe mass spectral features and a sample set of blood-based samples.

4. The method of clause 3, comprising repeating step c) to identify asecond set of features which are associated with a second biologicalfunction and repeating step d) by computing a biological function scorefor the second biological function.5. The method of clause 3 or 4, wherein the biological functioncomprises acute response, wound healing, or complement system.6. The method of clause 3, wherein steps a), b), and d) are performedfor a second set of blood-based samples.7. A method of evaluation of a biological process of a human, comprisingthe steps of:a) performing the process of clause 4 on a development set of samplesand obtaining biological function scores for each of the members of thedevelopment set of samples for at least two different biologicalfunctions;b) developing a classifier from the biological function scores for thedevelopment set of samples; andc) performing mass spectrometry of a blood-based sample from the humanand obtaining feature values for sets of mass spectral featuresassociated with the at least two different biological functions, andd) computing a biological function score for each of the at least twobiological functions for the blood-based sample from the human, ande) classifying the sample with the classifier developed in step b) onthe biological function scores computed in step d).8. The method of clause 7, further comprising generating a class labelfor the sample with a classifier trained from mass spectrometry data ofthe development set of samples and class labels associated with thedevelopment set of samples.9. The method of clause 7, wherein the classifier developed in step b)is organized to classify a sample based on a threshold in the biologicalfunction score.10. The method of clause 7, wherein the development set of samplescomprises a set of samples from melanoma patients treated with an immunecheckpoint inhibitor.11. A method of classifier development, comprising the steps of:

a) obtaining a development set of blood-based samples from a pluralityof humans;

b) conducting mass spectrometry on the development set of samples andobtaining mass spectrometry data including feature values of a set ofmass spectrometry features;

c) identifying associations of sets of the mass spectrometry featureswith at least one biological function;

d) computing a biological function score for each member in thedevelopment sample set by projecting a feature table containing featurevalues of the mass spectral features which are associated with thebiological function onto the direction of a first principal componentvector obtained from the mass spectral features and a development sampleset or alternatively on a second sample set; and

e) training a classifier with the biological function score for the atleast one biological function.

12. The method of clause 11, wherein steps c) and d) are performed toidentify at least two sets of mass spectrometry features with at leasttwo biological functions and computing at least two biological functionscores for each member in the development sample set; and wherein stepe) comprises training the classifier with the at least two biologicalfunction scores.13. The method of clause 12, wherein the training step e) comprises theprocedure of FIG. 8.14. The method of clause 3, further comprising computing a projection ofthe feature table containing feature values of the mass spectralfeatures which are associated with the biological function onto thedirection of a second principal component vector obtained from the massspectral features and the sample set.15. The method of clause 3, wherein the first principal component vectoris computed from a principal component analysis procedure over manydifferent realizations of subsets of the sample set.16. The method of clause 15, further comprising the step of iterativelyaveraging and normalizing first principal components until a baggedfirst principal component vector û₁ is obtained.

The appended claims are provided as further descriptions of thedisclosed inventions.

APPENDICES

APPENDIX A Feature Definitions Left Center Right 3073.625 3085.6653097.705 3098.586 3109.891 3121.197 3123.546 3138.669 3153.793 3190.7933210.615 3230.436 3230.73 3242.623 3254.516 3255.103 3264.647 3274.1913296.802 3316.77 3336.739 3349.072 3363.608 3378.144 3380.2 3391.9463403.692 3405.16 3420.283 3435.407 3435.7 3445.097 3454.494 3454.7883465.359 3475.931 3531.431 3553.896 3576.36 3581.059 3593.099 3605.1383665.631 3679.286 3692.941 3693.235 3702.631 3712.028 3712.616 3723.3343734.052 3745.211 3754.755 3764.299 3764.592 3775.604 3786.616 3804.5293817.744 3830.958 3831.545 3841.53 3851.514 3876.474 3887.486 3898.4993914.356 3928.158 3941.959 3942.253 3953.412 3964.571 3994.23 4008.6194023.008 4024.182 4031.67 4039.159 4039.452 4050.464 4061.476 4086.4374098.77 4111.104 4111.308 4118.525 4125.742 4126.043 4133.109 4140.1764198.812 4209.788 4220.764 4255.194 4264.44 4273.687 4273.837 4286.1664298.495 4328.264 4340.217 4352.17 4352.32 4360.815 4369.31 4369.6114380.962 4392.314 4393.817 4408.627 4423.436 4424.789 4431.179 4437.5694437.87 4459.22 4480.57 4496.507 4506.58 4516.654 4538.304 4545.074551.836 4552.738 4564.991 4577.245 4577.997 4590.1 4602.203 4616.9384625.282 4633.627 4633.927 4643.324 4652.721 4664.148 4675.274 4686.44690.609 4717.673 4744.736 4746.54 4756.162 4765.785 4766.386 4773.1524779.918 4780.218 4791.194 4802.17 4802.621 4817.881 4833.142 4836.2994855.995 4875.691 4880.953 4891.177 4901.401 4909.52 4918.316 4927.1114927.261 4937.636 4948.01 4948.16 4963.045 4977.93 4987.552 4998.9795010.405 5011.007 5020.103 5029.199 5029.65 5041.077 5052.504 5053.5565067.839 5082.123 5087.535 5104.074 5120.613 5120.913 5129.182 5137.4525137.602 5144.819 5152.036 5152.487 5157.824 5163.162 5163.312 5168.1235172.935 5173.085 5179.776 5186.466 5187.218 5197.818 5208.417 5209.775223.753 5237.736 5238.036 5247.734 5257.432 5275.624 5289.757 5303.895347.191 5358.543 5369.894 5370.646 5377.186 5383.726 5383.877 5389.8155395.754 5395.905 5403.422 5410.94 5411.09 5416.277 5421.464 5421.6155429.809 5438.003 5439.807 5449.204 5458.601 5463.713 5472.057 5480.4025481.454 5495.813 5510.171 5510.472 5521.222 5531.972 5538.738 5549.9395561.14 5561.29 5570.011 5578.731 5666.235 5673.903 5681.571 5685.0175691.464 5697.911 5698.296 5705.753 5713.211 5713.499 5720.428 5727.3565727.548 5733.899 5740.25 5740.923 5748.044 5755.165 5756.127 5762.3815768.636 5769.502 5776.671 5783.84 5787.496 5795.483 5803.469 5803.955815.979 5828.007 5828.68 5842.055 5855.431 5856.297 5867.218 5878.145879.39 5889.157 5898.924 5899.02 5910.808 5922.595 5930.871 5950.2125969.553 5978.694 5997.458 6016.222 6016.895 6026.806 6036.717 6082.7136090.94 6099.167 6099.552 6108.597 6117.642 6117.738 6122.213 6126.6876145.74 6153.101 6160.462 6161.136 6170.373 6179.611 6184.807 6192.9386201.069 6201.261 6209.585 6217.908 6218.004 6226.039 6234.074 6244.0816252.693 6261.305 6262.46 6267.993 6273.526 6273.718 6283.244 6292.7716292.867 6301.238 6309.61 6309.706 6315.48 6321.253 6321.927 6331.7426341.556 6349.736 6357.77 6365.805 6379.18 6386.108 6393.037 6393.2296399.965 6406.7 6408.625 6437.829 6467.033 6467.129 6485.171 6503.2146519.476 6534.342 6549.209 6560.275 6567.684 6575.093 6577.691 6589.4316601.17 6603.961 6611.61 6619.26 6620.319 6634.319 6648.32 6648.5126657.076 6665.64 6668.623 6680.651 6692.68 6719.237 6731.65 6744.0636754.648 6761.047 6767.446 6767.638 6773.171 6778.704 6778.8 6788.9526799.104 6799.97 6808.919 6817.868 6824.892 6836.679 6848.467 6848.9486859.629 6870.31 6873.797 6881.433 6889.07 6890.609 6897.599 6904.5896914.565 6921.586 6928.606 6933.102 6941.477 6949.853 6950.469 6956.756963.032 6963.34 6970.545 6977.75 6978.92 6992.222 7005.524 7014.2697022.06 7029.85 7029.912 7034.592 7039.272 7039.395 7045.154 7050.9127067.293 7073.79 7080.287 7119.824 7147.228 7174.633 7177.589 7188.9517200.314 7235.047 7244.438 7253.83 7254.384 7260.204 7266.023 7266.0857273.598 7281.111 7281.173 7286.839 7292.504 7292.812 7300.633 7308.4547310.117 7318.369 7326.622 7326.991 7333.581 7340.17 7352.179 7359.2617366.343 7379.768 7393.009 7406.249 7406.742 7419.613 7432.484 7432.6077441.075 7449.543 7449.604 7456.44 7463.276 7463.584 7474.361 7485.1387497.824 7510.356 7522.889 7523.751 7535.975 7548.2 7731.041 7738.8017746.561 7761.341 7779.077 7796.813 7874.347 7882.876 7891.405 7904.9547912.836 7920.719 8007.424 8015.07 8022.717 8134.845 8146.885 8158.9258173.99 8183.91 8193.83 8195.752 8206.991 8218.23 8238.737 8253.9178269.098 8308.327 8315.347 8322.368 8322.784 8330.844 8338.904 8353.4068363.536 8373.667 8380.133 8391.495 8402.857 8404.767 8413.388 8422.018422.133 8430.231 8438.33 8457.421 8464.072 8470.723 8470.846 8477.5588484.271 8485.195 8492.061 8498.928 8506.934 8513.585 8520.236 8520.5448531.198 8541.852 8554.723 8564.761 8574.799 8575.476 8585.268 8595.068618.77 8631.702 8644.635 8649.87 8660.554 8671.239 8671.855 8696.1198720.383 8720.568 8728.512 8736.456 8736.518 8745.817 8755.116 8756.3488770.604 8784.861 8791.574 8796.962 8802.351 8802.474 8822.181 8841.8878861.964 8871.848 8881.732 8883.826 8890.538 8897.251 8897.436 8901.6548905.873 8905.934 8928.258 8950.582 8967.272 8974.415 8981.559 8988.1498997.971 9007.794 9010.011 9020.295 9030.58 9030.764 9038.216 9045.6689067.961 9077.198 9086.436 9091.547 9097.798 9104.049 9105.613 9109.4569113.299 9115.171 9134.336 9153.501 9175.08 9187.076 9199.073 9199.1229208.31 9217.498 9217.991 9226.317 9234.643 9234.742 9244.546 9254.359254.941 9263.932 9272.924 9273.899 9284.974 9296.05 9310.761 9318.8659326.969 9344.962 9359.289 9373.615 9387.662 9395.293 9402.923 9410.8179430.014 9449.211 9475.524 9484.41 9493.296 9494.536 9504.042 9513.5499520.898 9534.803 9548.707 9559.484 9576.179 9592.874 9615.006 9641.1799667.352 9689.387 9720.901 9752.414 9784.265 9793.021 9801.777 9840.8959862.594 9884.294 9908.49 9918.931 9929.371 9929.66 9941.495 9953.33110002.41 10012.36 10022.32 10066.88 10079.24 10091.61 10091.7 10102.2410112.77 10120.09 10135.29 10150.49 10150.78 10162.62 10174.45 10174.6510185.23 10195.82 10195.91 10210.35 10224.78 10225.16 10236.04 10246.9110250.09 10263.03 10275.97 10276.55 10285.11 10293.68 10294.45 10304.3110314.17 10314.27 10321.34 10328.41 10333.03 10346.26 10359.49 10359.6910365.85 10372 10409.53 10418.53 10427.52 10436.76 10448.55 10460.3410465.73 10476.5 10487.28 10487.47 10493.58 10499.69 10500.46 10508.410516.34 10517.4 10533.61 10549.83 10568.97 10588.56 10608.14 10615.8410636.81 10657.79 10705.55 10734.07 10762.59 10773.07 10782.59 10792.1210792.22 10801.79 10811.36 10827.72 10846.77 10865.83 10912.98 10923.5610934.15 10934.82 10944.01 10953.2 10953.3 10961.23 10969.17 11032.6811044.95 11057.22 11057.51 11067.08 11076.66 11091.28 11103.93 11116.5911137.08 11149.06 11161.04 11187.6 11197.22 11206.85 11217.43 11228.0611238.7 11288.09 11306.44 11324.78 11357.26 11375.9 11394.54 11426.1811445.9 11465.63 11465.99 11480.52 11495.05 11498.47 11526.91 11555.3611560.53 11576.4 11592.27 11602.51 11632.22 11661.93 11662.77 11686.2811709.8 11712.08 11733.43 11754.78 11756.23 11786.66 11817.09 11817.9311834.77 11851.61 11868.93 11898.52 11928.11 11928.7 11952.25 11975.811976.8 12002.53 12028.25 12219.68 12232.71 12245.74 12271.47 12290.8512310.22 12310.89 12321.25 12331.6 12340.29 12351.31 12362.34 12400.4312412.62 12424.81 12433.83 12457.39 12480.94 12536.4 12565.8 12595.212597.2 12613.41 12629.61 12647.98 12674.21 12700.44 12716.47 12738.0212759.57 12761.24 12785.79 12810.35 12829.73 12873.16 12916.59 12935.6312967.54 12999.44 13051.23 13080.96 13110.69 13117.71 13134.41 13151.1213258.69 13274.73 13290.77 13304.46 13323.17 13341.88 13347.56 13364.613381.64 13510.26 13524.96 13539.66 13551.35 13567.72 13584.09 13595.1213614.66 13634.21 13703.7 13721.07 13738.44 13740.45 13762.16 13783.8813784.55 13798.24 13811.94 13826.64 13842.84 13859.05 13864.06 13882.9313901.81 13903.15 13916.01 13928.87 13929.87 13943.07 13956.27 13958.6113983.66 14008.72 14015.73 14042.8 14069.86 14076.2 14097.59 14118.9714124.31 14149.03 14173.76 14178.77 14198.98 14219.19 14231.55 14254.6114277.66 14281.33 14306.56 14331.78 14405.61 14433.68 14461.74 14462.4114488.47 14514.53 14515.19 14540.58 14565.97 14567.31 14594.54 14621.7714751.73 14784.47 14817.21 14857.63 14884.53 14911.42 15002.96 15026.6815050.4 15527.48 15563.22 15598.97 15613 15629.04 15645.07 15719.5815751.48 15783.39 16465.93 16502.17 16538.42 16610.92 16630.13 16649.3416999.46 17032.54 17065.61 17104.03 17148.13 17192.23 17225.64 17270.9117316.18 17344.57 17394.52 17444.47 17445.13 17476.37 17507.61 17569.0817604.33 17639.57 17774.54 17815.47 17856.39 17982.34 18031.12 18079.918232.58 18275.34 18318.1 18593.72 18636.99 18680.25 18704.31 18728.6918753.08 18816.22 18850.13 18884.04 19339.07 19373.15 19407.22 19407.5619463.85 19520.15 19522.82 19575.27 19627.72 19843.56 19992.15 20140.7420482.31 20562.33 20642.34 20886.89 20945.69 21004.49 21005.16 21061.9521118.75 21119.42 21170.37 21221.31 21221.98 21275.44 21328.89 21330.8921377.17 21423.44 21436.13 21485.91 21535.69 21642.26 21687.7 21733.1321733.47 21760.7 21787.93 21788.93 21816.49 21844.05 22967.23 23036.0523104.87 23106.91 23146.16 23185.42 23187.46 23249.14 23310.82 23311.8423356.7 23401.56 23407.68 23468.85 23530.03 27874.33 27944.17 28014.0128015.03 28082.32 28149.61

APPENDIX B Features included in the classifiers (Example 1) Whole WholeSet Set Approach Approach Approach Approach Approach Approach 1 2 3 4 12 3445 3265 3243 3110 3243 3110 3465 3554 3703 3364 3703 3703 3703 37033723 3593 3723 3723 3928 3723 3928 3703 3755 3755 4050 3755 4381 37233842 3776 4133 3776 4409 3776 3928 3928 4286 3928 5416 3928 3953 39534565 3953 5472 4133 4050 4050 4718 4050 5496 4264 4409 4133 4818 41335550 4381 4545 4756 5020 4264 5762 4409 4590 4791 5168 4381 5777 44315020 5020 5180 4409 5816 4545 5145 5068 5449 4590 5842 4590 5168 51045472 5020 5867 4756 5449 5145 5521 5068 5889 4791 5550 5550 5550 51045911 4999 5706 5570 5674 5129 5950 5020 5777 5734 5748 5168 5997 50685816 5762 5777 5390 6027 5104 5842 5842 5867 5449 7045 5145 5867 58675889 5472 7274 5472 5889 5889 5911 5550 7301 5521 5911 5911 5950 55707318 5550 5950 5950 5997 5762 7739 5570 5997 5997 6027 5777 7883 57066027 6091 6091 5816 8254 5734 6091 6109 6210 5842 8661 5762 6153 61706568 5867 8696 5777 6210 6210 6860 5889 8729 5795 6568 6568 6881 59118771 5816 6860 6860 6941 5950 8872 5842 6992 6881 6971 5997 8891 58677260 7318 7045 6091 8998 5889 7274 8391 7287 6170 9020 5911 7287 85317441 6210 9038 5950 7318 9109 7779 6789 9098 5997 8771 11446 8771 68609226 6091 9098 11481 9134 6941 10163 6210 9134 11527 9187 7045 102856634 9319 11686 9226 7318 10346 6681 9430 11733 9285 7883 10847 673210210 11787 9319 7913 11067 6761 11446 11835 9430 8391 11104 6837 1148111899 9641 8413 11376 6881 11527 11952 9941 8492 11446 6898 11632 1200310102 8771 11481 6957 11686 13134 10185 9020 11527 7074 11733 1332310210 9109 11576 7318 11787 13721 10285 9187 11632 7334 11835 1376210346 9226 11686 7739 11899 13843 10449 9535 11733 7883 11952 1703310734 10135 11787 8391 12003 18275 11045 10285 11835 8413 12413 1863711104 11045 11899 8565 12873 18729 11197 11067 11952 8661 13134 1885011835 11197 12003 8696 13323 19992 11952 11446 13568 8729 13568 2335712003 11481 13615 8771 13615 23469 12351 11527 13721 8797 13721 1241311576 13883 8872 14541 12674 11632 13916 8891 14784 13134 11686 182758998 17033 13323 11733 23357 9098 17148 13365 11787 23469 9109 1827513568 11835 9226 18637 13615 11899 9504 18729 13721 11952 10102 2117013762 12003 11067 21486 14255 12351 11104 23036 15629 12968 11376 2314617033 13134 11446 23357 17148 13323 11481 23469 17271 13365 11527 1747613568 11576 18275 13615 11632 18637 13721 11686 18729 13762 11733 1999213883 11787 21062 15629 11835 21170 17033 11899 21486 17476 11952 2303618031 12003 23146 18275 12413 18637 13134 18729 13615 18850 13721 1999213762 20946 13984 21062 18275 21170 18637 21275 18729 23357 18850 2346919992 21062 23357 23469

APPENDIX C Subset of features used in the classifier of Example 2 30863317 3755 3776 3818 3928 3953 4264 4545 4675 4756 4773 5068 5104 51985403 5472 5674 5706 5795 6301 6400 6534 6612 6634 6681 6789 6837 68986941 6957 7420 8315 8464 8531 8565 8797 8891 8998 9098 9187 9226 92459319 9395 9504 9941 10079 10263 10346 10961 12457 13798 13883 1391613984 14098 17033 17604 17815 23249

APPENDIX D Mass spectral features used in Classifier 1 or Classifier 2of Example 6 3465 7274 3679 7287 3703 7334 3842 7536 4032 7883 4133 79134545 8331 4590 8391 4718 8902 4818 8928 4891 9430 4999 9641 5068 100125129 10924 5158 12291 5180 12351 5290 12413 5377 13275 5430 13762 549613798 5550 14098 6438 14541 6681 14595 6761 15751 6809 16630 6881 170336898 18275 6992 18637 7022 23249 7035

1. (canceled)
 2. A method of guiding melanoma patient treatment withimmunotherapy drugs, comprising: a) conducting mass spectrometry on ablood-based sample of the patient and obtaining mass spectrometry data;(b) obtaining integrated intensity values in the mass spectrometry dataof a multitude of mass-spectral features; and (c) operating on the massspectral data with a programmed computer implementing a classifier;wherein in the operating step the classifier compares the integratedintensity values with feature values of a reference set of class-labeledmass spectral data obtained from blood-based samples obtained from amultitude of other melanoma patients treated with an antibody drugblocking ligand activation of programmed cell death 1 (PD-1) with aclassification algorithm and generates a class label for the sample,wherein the class label “Good” or the equivalent predicts the patient islikely to obtain similar benefit from a combination therapy comprisingan antibody drug blocking ligand activation of PD-1 and an antibody drugtargeting CTLA4 and is therefore guided to a monotherapy of an antibodydrug blocking ligand activation of PD-1, whereas a class label of “NotGood” or the equivalent indicates the patient is likely to obtaingreater benefit from the combination therapy as compared to themonotherapy of an antibody drug blocking ligand activation of programmedcell death 1 (PD-1) and is therefore guided to the combination therapy,wherein the mass spectral features include a multitude of featureslisted in Appendix A, Appendix B or Appendix C.
 3. The method of claim2, wherein the classifier is obtained from filtered mini-classifierscombined using a regularized combination method.
 4. The method of claim3, wherein the regularized combination method comprises repeatedlyconducting logistic regression with extreme dropout on the filteredmini-classifiers.
 5. A method of guiding melanoma patient treatment withimmunotherapy drugs, comprising: a) conducting mass spectrometry on ablood-based sample of the patient and obtaining mass spectrometry data;(b) obtaining integrated intensity values in the mass spectrometry dataof a multitude of mass-spectral features; and (c) operating on the massspectral data with a programmed computer implementing a classifier;wherein in the operating step the classifier compares the integratedintensity values with feature values of a reference set of class-labeledmass spectral data obtained from blood-based samples obtained from amultitude of other melanoma patients treated with an antibody drugblocking ligand activation of programmed cell death 1 (PD-1) with aclassification algorithm and generates a class label for the sample,wherein the class label “Good” or the equivalent predicts the patient islikely to obtain similar benefit from a combination therapy comprisingan antibody drug blocking ligand activation of PD-1 and an antibody drugtargeting CTLA4 and is therefore guided to a monotherapy of an antibodydrug blocking ligand activation of PD-1, whereas a class label of “NotGood” or the equivalent indicates the patient is likely to obtaingreater benefit from the combination therapy as compared to themonotherapy of an antibody drug blocking ligand activation of programmedcell death 1 (PD-1) and is therefore guided to the combination therapy,wherein the classifier is obtained from filtered mini-classifierscombined using a regularized combination method, and wherein themini-classifiers are filtered in accordance with criteria listed inTable
 10. 6. The method of claim 2, wherein the classifier comprises anensemble of tumor classifiers combined in a hierarchical manner.
 7. Themethod of claim 2, wherein the relatively greater benefit from thecombination therapy label means significantly greater (longer) overallsurvival as compared to monotherapy.
 8. The method of claim 2, whereinthe reference set comprise a set of class-labeled mass spectral data ofa development set of samples having either the class label Early or theequivalent or Late or the equivalent, wherein the samples having theclass label Early are comprised of samples having relatively shorteroverall survival on treatment with nivolumab as compared to sampleshaving the class label Late.
 9. The method of claim 2, wherein the massspectral data is acquired from at least 100,000 laser shots performed onthe sample using MALDI-TOF mass spectrometry.
 10. The method of claim 2,wherein the mass-spectral features are selected according to theirassociation with the biological functions Acute Response and WoundHealing. 11.-20. (canceled)